CN112435757B - Prediction device and system for acute hepatitis - Google Patents
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- 208000006454 hepatitis Diseases 0.000 title claims abstract description 62
- 231100000354 acute hepatitis Toxicity 0.000 title claims abstract description 56
- 206010025482 malaise Diseases 0.000 claims abstract description 81
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 38
- 201000010099 disease Diseases 0.000 claims abstract description 30
- 230000010354 integration Effects 0.000 claims abstract description 10
- 238000003066 decision tree Methods 0.000 claims description 27
- HVYWMOMLDIMFJA-DPAQBDIFSA-N cholesterol Chemical compound C1C=C2C[C@@H](O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H]([C@H](C)CCCC(C)C)[C@@]1(C)CC2 HVYWMOMLDIMFJA-DPAQBDIFSA-N 0.000 claims description 6
- 206010012601 diabetes mellitus Diseases 0.000 claims description 6
- 208000019622 heart disease Diseases 0.000 claims description 6
- 231100000283 hepatitis Toxicity 0.000 claims description 6
- 230000002996 emotional effect Effects 0.000 claims description 5
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 3
- 239000008280 blood Substances 0.000 claims description 3
- 210000004369 blood Anatomy 0.000 claims description 3
- 230000036772 blood pressure Effects 0.000 claims description 3
- 235000012000 cholesterol Nutrition 0.000 claims description 3
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 239000001301 oxygen Substances 0.000 claims description 3
- 230000036387 respiratory rate Effects 0.000 claims description 3
- 208000028782 Hereditary disease Diseases 0.000 claims 2
- 208000024556 Mendelian disease Diseases 0.000 claims 2
- 230000034994 death Effects 0.000 abstract description 4
- 230000000694 effects Effects 0.000 abstract description 3
- 238000012706 support-vector machine Methods 0.000 description 22
- 238000010586 diagram Methods 0.000 description 6
- 238000000034 method Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 208000007788 Acute Liver Failure Diseases 0.000 description 3
- 206010000804 Acute hepatic failure Diseases 0.000 description 3
- 231100000836 acute liver failure Toxicity 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 208000026350 Inborn Genetic disease Diseases 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 208000016361 genetic disease Diseases 0.000 description 2
- 208000019423 liver disease Diseases 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 229910002056 binary alloy Inorganic materials 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 230000035622 drinking Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 210000003494 hepatocyte Anatomy 0.000 description 1
- 230000003908 liver function Effects 0.000 description 1
- 230000017074 necrotic cell death Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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Abstract
The invention provides a prediction device for acute hepatitis, comprising: a data acquisition module (11) for acquiring data related to an acute hepatitis patient; the data processing module (12) is connected with the data acquisition module (11) and is used for processing the related data of the acute hepatitis patient to generate characteristic data; the prediction module (13) is connected with the data processing module (12) and is used for predicting the characteristic data to obtain a plurality of preliminary non-sickness probabilities and sickness probabilities; and the integration module (14) is connected with the prediction module (13) and is used for counting the preliminary non-sickness probabilities and sickness probabilities to obtain final non-sickness probabilities and sickness probabilities of patients with acute hepatitis. The invention also provides a prediction system for acute hepatitis. The invention can conveniently and rapidly predict the acute hepatitis disease, strives for precious treatment time for patients, and reduces the probability and death rate of sudden acute hepatitis disease, thereby achieving the effect of disease early warning.
Description
Technical Field
The invention relates to the field of medical data processing, in particular to a device and a system for predicting acute hepatitis.
Background
Acute liver failure in acute hepatitis is a multi-cause liver disease, and the clinical manifestation of acute liver failure patients is massive hepatocyte necrosis in a short time, and severe liver function damage, and a series of liver diseases may be induced. At present, no specific treatment method for acute liver failure exists, and the death rate is high because the disease has short onset time. It is therefore important to be able to predict the disease. At present, biological reagents are mainly adopted for detection, but the detection means needs to purchase a kit and guide special medical staff, so that the detection of a patient cannot be conveniently and quickly finished, and the best treatment or rescue opportunity of the patient is likely to be missed.
Therefore, there is a need for an acute hepatitis prediction apparatus and system to address the above problems.
Disclosure of Invention
Accordingly, an objective of the embodiments of the present invention is to provide a device and a system for predicting acute hepatitis, which aims to solve the problem that the acute hepatitis diseases cannot be detected conveniently and rapidly in the prior art.
In order to achieve the above object, a first technical solution adopted by the present invention is to provide a device for predicting acute hepatitis, comprising:
a data acquisition module 11 for acquiring relevant data of the patient with acute hepatitis;
the data processing module 12 is connected with the data acquisition module 11 and is used for processing the related data of the acute hepatitis patient and generating characteristic data;
the prediction module 13 is connected with the data processing module 12 and is used for predicting the generated characteristic data to obtain a plurality of preliminary non-sickness probabilities and sickness probabilities;
And the integration module 14 is connected with the prediction module 13 and is used for counting the plurality of preliminary non-sickness probabilities and sickness probabilities to obtain final non-sickness probabilities and sickness probabilities of the acute hepatitis patient.
Preferably, the related data includes: continuous data and discrete data;
the continuous data includes: pulse rate, heart rate, respiratory rate, blood oxygen saturation, age, height, gender, body mass index, blood pressure, and cholesterol index;
The discrete data includes: emotional state, drinking, diabetes, familial history of inherited diabetes, hepatitis, history of inherited hepatitis, heart disease, and history of inherited heart disease.
Preferably, the apparatus further comprises:
a model training module 15, connected to the prediction module 13, for:
inputting continuous data of the sample set into a training algorithm of the SVM model to obtain optimal weight parameters,
Inputting the discrete data of the sample set into a training algorithm of a decision tree model to construct an optimal classification decision tree,
Inputting continuous data and discrete data of the sample set into a training algorithm of the MLP model to obtain optimal weight parameters;
wherein the sample set comprises the relevant data for a plurality of diseased and non-diseased individuals.
Preferably, the data processing module 12 is configured to:
Regularizing the continuous data, in particular,
Using the formulaCalculating the intermediate value of a certain index x;
Wherein x is the original value of a certain index, x_min is the empirical minimum value of a certain index, x_max is the empirical maximum value of a certain index, and x' is the intermediate value of a certain index x;
performing binary processing on the x' to obtain characteristic data: x ' is set to 1 when x ' is greater than 1 and to 0 when x ' is less than 0.
Preferably, the data processing module 12 is further configured to:
and performing binary coding processing on the discrete data to obtain characteristic data.
Preferably, the prediction module 13 is configured to:
predicting the continuous data by adopting an SVM model to obtain the preliminary non-sickness probability and sickness probability of the continuous data;
Predicting the discrete data by adopting a decision tree model to obtain preliminary non-sickness probability and sickness probability of the discrete data; and
And predicting the continuous data and the discrete data by adopting an MLP model to obtain the combined preliminary non-sickness probability and sickness probability.
Preferably, the integration module 14 is configured to:
Inputting the preliminary non-sickness probability and sickness probability of the continuous data, the preliminary non-sickness probability and sickness probability of the discrete data and the combined preliminary non-sickness probability and sickness probability into an integrated learning model, predicting final non-sickness probability p 0 and sickness probability p 1, the specific formulas of the non-sickness probability p 0 and the sickness probability p 1 are as follows,
Wherein, P 10 is the non-sickness probability predicted by the SVM model, P 20 is the non-sickness probability predicted by the decision tree model, P 30 is the non-sickness probability predicted by the MLP model, P 11 is the sickness probability predicted by the SVM model, P 21 is the sickness probability predicted by the decision tree model, P 31 is the sickness probability predicted by the MLP model, and P 0 is the final non-sickness probability;
wherein, P 11 is the disease probability predicted by the SVM model, P 21 is the disease probability predicted by the decision tree model, P 31 is the disease probability predicted by the MLP model, P 10 is the non-disease probability predicted by the SVM model, P 20 is the non-disease probability predicted by the decision tree model, P 30 is the non-disease probability predicted by the MLP model, and P 1 is the final disease probability.
Preferably, the data acquisition module 11 is configured to:
the data is obtained by a browser and/or a WeChat applet and/or an application.
In order to achieve the above object, the second technical scheme adopted by the present invention is as follows: provided is a system for predicting acute hepatitis, comprising:
A terminal and a device which is connected with the terminal in a communication way and realizes the modules;
The terminal comprises: at least one of a mobile terminal, a tablet computer, a notebook computer, a desktop computer and an intelligent television.
The invention provides a prediction device and a system for acute hepatitis, which are used for acquiring relevant data of patients with acute hepatitis; processing the relevant data of the acute hepatitis patient to generate characteristic data; predicting the characteristic data to obtain a plurality of preliminary non-sickness probabilities and sickness probabilities; and counting the plurality of preliminary non-diseased probabilities and diseased probabilities to obtain final non-diseased probabilities and diseased probabilities of the acute hepatitis patients, so that the acute hepatitis diseases can be conveniently and rapidly predicted, precious treatment time is strived for the patients, the probability and death rate of sudden acute hepatitis diseases are reduced, and the effect of disease early warning is achieved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic block diagram of a device for predicting acute hepatitis according to a first embodiment of the present invention;
fig. 2 is another schematic block diagram of a device for predicting acute hepatitis according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a system for predicting acute hepatitis according to a third embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
The invention is further described in detail below with reference to the accompanying drawings and specific examples.
Referring to fig. 1, fig. 1 is a schematic block diagram of a device for predicting acute hepatitis. In an embodiment of the present invention, the apparatus for predicting acute hepatitis includes:
a data acquisition module 11 for acquiring relevant data of the patient with acute hepatitis;
the data processing module 12 is connected with the data acquisition module 11 and is used for processing the related data of the acute hepatitis patient and generating characteristic data;
the prediction module 13 is connected with the data processing module 12 and is used for predicting the generated characteristic data to obtain a plurality of preliminary non-sickness probabilities and sickness probabilities;
The integration module 14 is connected with the prediction module 13 and is used for counting the plurality of preliminary non-sickness probabilities and sickness probabilities to obtain final non-sickness probabilities and sickness probabilities;
Specifically, the data acquisition module 11 is configured to acquire relevant data of the disease by means of a web browser, a WeChat applet, an application program, and the like. Relevant data for the disease include: physical index data such as pulse rate, heart rate, respiratory rate, blood oxygen saturation, age, height, sex, body mass index, blood pressure (high and low pressure), cholesterol index, emotional state, whether to drink, whether to have diabetes, whether to have a history of familial genetic diabetes, whether to have hepatitis, whether to have a history of hepatitis genetic disease, whether to have heart disease, and whether to have a history of heart disease genetic disease;
in particular, the data processing module 12 employs regularization to process the continuous data, in particular,
(1) Using the formulaCalculating the intermediate value of a certain index x;
Wherein x is the original value of a certain index, x_min is the empirical minimum value of a certain index, x_max is the empirical maximum value of a certain index, and x' is the intermediate value of a certain index x;
(2) Performing binary processing on the x' to obtain characteristic data: setting x 'to 1 when x' is greater than 1 and setting x 'to 0 when x' is less than 0;
For example, calculating a heart rate index, wherein the original heart rate value is 120, the minimum empirical value is 60, and the maximum empirical value is 100, and then calculating the initial value of the heart rate index by adopting the formula in (1) to obtain 1.5, and performing binary processing to obtain 1 heart rate index value, namely obtaining 1 characteristic data;
Specifically, the data processing module 12 is further configured to process the discrete data by binary coding to obtain feature data, for example, three types of emotional states, namely, good, general and frustration, respectively corresponding to decimal codes 0, 1 and 2. If the emotional state of the patient is general, firstly taking a value of 1, and then converting the 1 into binary system to obtain 01, namely generating characteristic data of 01;
Specifically, the prediction module 13 is configured to predict continuous data by adopting SVM (Support Vector Machine) model, so as to obtain preliminary non-diseased probability p 10 and diseased probability p 11 of the continuous data; predicting discrete data by adopting a decision tree model to obtain preliminary non-illness probability p 20 and illness probability p 21 of the discrete data; predicting continuous data and discrete data by adopting an MLP (Multi-layer Perceptron) model to obtain a combined preliminary non-illness probability p 30 and an illness probability p 31;
Specifically, the integration module 14 is configured to input the preliminary non-diseased probability P 10 and the diseased probability P 11 of the continuous data, the preliminary non-diseased probability P 20 and the diseased probability P 21 of the discrete data, and the combined preliminary non-diseased probability P 30 and the diseased probability P 31 into the integrated learning model, and predict the final non-diseased probability P 0 and the diseased probability P 1 of the acute hepatitis patient;
the specific formula is as follows,
Wherein, P 10 is the non-diseased probability predicted by SVM (Support Vector Machine) model, P 20 is the non-diseased probability predicted by decision tree model, P 30 is the non-diseased probability predicted by MLP (Multi-layer Perceptron) model, P 11 is the diseased probability predicted by SVM (Support Vector Machine) model, P 21 is the diseased probability predicted by decision tree model, P 31 is the diseased probability predicted by MLP (Multi-layer Perceptron) model, and P 0 is the final non-diseased probability;
In the above-described non-illness probability calculation formula, The arithmetic average value of the non-illness probability is predicted for three models of SVM, decision tree and MLP,Predicting a harmonic mean value of the non-sickness probability for three models of SVM, decision tree and MLP, and counting the arithmetic mean value of the two mean values again to ensure that the final counting result has higher accuracy;
Wherein, P 11 is the disease probability predicted by SVM (Support Vector Machine) model, P 21 is the disease probability predicted by decision tree model, P 31 is the disease probability predicted by MLP (Multi-layer Perceptron) model, P 10 is the non-disease probability predicted by SVM (Support Vector Machine) model, P 20 is the non-disease probability predicted by decision tree model, P 30 is the non-disease probability predicted by MLP (Multi-layer Perceptron) model, and P 1 is the final disease probability;
In the above-described disease probability calculation formula, The arithmetic mean of the disease probabilities is predicted for three models of SVM, decision tree and MLP,And predicting the harmonic mean value of the illness probability for three models of the SVM, the decision tree and the MLP, and carrying out arithmetic mean value statistics on the two mean values again to ensure that the final statistical result has higher accuracy.
Referring to fig. 2, fig. 2 is another schematic block diagram of a device for predicting acute hepatitis. In an embodiment of the present invention, the apparatus for predicting acute hepatitis includes:
a data acquisition module 21 for acquiring data related to the patient with acute hepatitis;
A data processing module 22, connected to the data acquisition module 21, for processing the relevant data of the acute hepatitis patient to generate feature data;
the prediction module 23 is connected with the data processing module 22 and is used for predicting the generated characteristic data to obtain a plurality of preliminary non-sickness probabilities and sickness probabilities;
The integration module 24 is connected with the prediction module 23 and is used for counting the plurality of preliminary non-sickness probabilities and sickness probabilities to obtain final non-sickness probabilities and sickness probabilities;
The model training module 25 is connected with the prediction module 23 and is used for training an SVM model, a decision tree model and an MLP model so as to optimize the prediction algorithm of the three models;
specifically, the data acquisition module 21, the data processing module 22, the prediction module 23, and the integration module 24 correspond to the data acquisition module 11, the data processing module 12, the prediction module 13, and the integration module 14 in fig. 1, respectively, and are not described herein again;
the model training module 25 is specifically implemented as follows:
Collecting relevant data of a plurality of individuals suffering from and not suffering from the illness, including continuous data such as heart rate and discrete data such as whether to drink or not, and forming a sample set; then inputting continuous data of the sample set into a training algorithm of the SVM model to obtain optimal weight parameters, inputting discrete data of the sample set into a training algorithm of the decision tree model to construct an optimal classification decision tree, and inputting continuous data and discrete data of the sample set into a training algorithm of the MLP model to obtain optimal weight parameters; through training the model, high accuracy of non-illness probability and illness probability prediction by adopting SVN model, decision tree model and MLP is ensured.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a prediction system for acute hepatitis. In an embodiment of the present invention, the acute hepatitis prediction system includes:
terminals 1 to N, and acute hepatitis prediction means communicatively connected to the terminals 1 to N, respectively;
the terminals 1 to N include: network terminals such as mobile terminals, tablet computers, notebook computers, desktop computers, intelligent televisions and the like;
Specifically, the terminals 1 to N are connected to and interact with the acute hepatitis prediction device through a wired or wireless communication manner. For example, a user uses a mobile phone to send data related to an acute hepatitis disease to an acute hepatitis prediction device; the acute hepatitis prediction device predicts after receiving the related data and returns the prediction result to the mobile phone. The user can check the predicted result of the acute hepatitis through the mobile phone.
The invention provides a prediction device and a system for acute hepatitis, which are used for acquiring relevant data of patients with acute hepatitis; processing the relevant data of the acute hepatitis patient to generate characteristic data; predicting the characteristic data to obtain a plurality of preliminary non-sickness probabilities and sickness probabilities; and counting the plurality of preliminary non-diseased probabilities and diseased probabilities to obtain final non-diseased probabilities and diseased probabilities of the acute hepatitis patients, so that the acute hepatitis diseases can be conveniently and rapidly predicted, precious treatment time is strived for the patients, the probability and death rate of sudden acute hepatitis diseases are reduced, and the effect of disease early warning is achieved.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and system may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules is merely a logical function division, and there may be additional divisions of actual implementation, e.g., multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules.
The integrated modules, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a readable storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned readable storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
It should be noted that, for the sake of simplicity of description, the foregoing method embodiments are all expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily all required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, and all the structural equivalents of the invention described in the specification and drawings are included in the scope of the invention, or the invention may be directly/indirectly applied to other related technical fields.
Claims (4)
1. A predictive device for acute hepatitis, the device comprising:
a data acquisition module (11) for acquiring data related to an acute hepatitis patient;
The data processing module (12) is connected with the data acquisition module (11) and is used for processing the related data of the acute hepatitis patient to generate characteristic data;
The prediction module (13) is connected with the data processing module (12) and is used for predicting the characteristic data to obtain a plurality of preliminary non-sickness probabilities and sickness probabilities;
The integration module (14) is connected with the prediction module (13) and is used for counting the preliminary non-sickness probabilities and sickness probabilities to obtain final non-sickness probabilities and sickness probabilities of patients with acute hepatitis;
the related data includes: continuous data and discrete data; the continuous data includes: pulse rate, heart rate, respiratory rate, blood oxygen saturation, age, height, gender, body mass index, blood pressure, and cholesterol index; the discrete data includes: emotional state, whether to drink, whether to have diabetes, whether to have a history of familial inherited diabetes, whether to have hepatitis, whether to have a history of hepatitis inherited disease, whether to have heart disease, and whether to have a history of heart disease inherited disease;
the data processing module (12) is configured to: regularizing the continuous data to obtain characteristic data, wherein the characteristic data comprises the following specific steps:
Using the formula Calculating an intermediate value x' of a certain index x;
Wherein x is the original value of a certain index, x_min is the empirical minimum value of a certain index, x_max is the empirical maximum value of a certain index, and x' is the intermediate value of a certain index x;
performing binary processing on the x' to obtain characteristic data: setting x 'to 1 when x' is greater than 1 and setting x 'to 0 when x' is less than 0;
the data processing module (12) is further configured to: performing binary coding processing on the discrete data to obtain characteristic data;
The prediction module (13) is configured to: predicting the continuous data by adopting an SVM model to obtain the preliminary non-sickness probability and sickness probability of the continuous data; predicting the discrete data by adopting a decision tree model to obtain preliminary non-sickness probability and sickness probability of the discrete data; predicting the continuous data and the discrete data by adopting an MLP model to obtain a combined preliminary non-sickness probability and sickness probability; the integration module (14) is configured to: inputting the preliminary non-sickness probability and sickness probability of the continuous data, the preliminary non-sickness probability and sickness probability of the discrete data and the combined preliminary non-sickness probability and sickness probability into an integrated learning model, predicting final non-sickness probability p 0 and sickness probability p 1, the specific formulas of the non-sickness probability p 0 and the sickness probability p 1 are as follows,
Wherein, P 10 is the non-sickness probability predicted by the SVM model, P 20 is the non-sickness probability predicted by the decision tree model, P 30 is the non-sickness probability predicted by the MLP model, P 11 is the sickness probability predicted by the SVM model, P 21 is the sickness probability predicted by the decision tree model, P 31 is the sickness probability predicted by the MLP model, and P 0 is the final non-sickness probability;
wherein, P 11 is the disease probability predicted by the SVM model, P 21 is the disease probability predicted by the decision tree model, P 31 is the disease probability predicted by the MLP model, P 10 is the non-disease probability predicted by the SVM model, P 20 is the non-disease probability predicted by the decision tree model, P 30 is the non-disease probability predicted by the MLP model, and P 1 is the final disease probability.
2. The apparatus of claim 1, wherein the apparatus further comprises:
a model training module 15, connected to the prediction module 13, for:
inputting continuous data of the sample set into a training algorithm of the SVM model to obtain weight parameters;
Inputting discrete data of the sample set into a training algorithm of a decision tree model to construct a classification decision tree;
Inputting continuous data and discrete data of the sample set into a training algorithm of the MLP model to obtain weight parameters;
wherein the sample set comprises the relevant data for a plurality of diseased and non-diseased individuals.
3. The apparatus according to claim 1, wherein the data acquisition module (11) is configured to:
the data is obtained by a browser and/or a WeChat applet and/or an application.
4. A system for predicting acute hepatitis, comprising:
a terminal, and the apparatus of any one of claims 1 to 3 communicatively connected to the terminal;
the terminal comprises: at least one of a mobile terminal, a desktop computer and an intelligent television.
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