Intelligent interrogation system based on XGBoost disease prediction and method
A kind of disease and intelligent technology, which is applied in the direction of medical automatic diagnosis, instrument, character and pattern recognition, etc., can solve the problem of inability to accurately analyze and predict massive patient data, and achieve the reduction of medical accidents, high accuracy and timeliness, and high Timeliness effect
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Embodiment 1
[0038] like figure 1 As shown, this embodiment proposes an intelligent consultation system based on XGBoost disease prediction, which includes a client 100 and a server 200 connected through a network.
[0039] The client 100 includes a consultation module 110 and a display module 120 .
[0040] The consultation module 110 is used to obtain the basic information and symptom information of the patient by simulating the question-and-answer interaction between the doctor and the patient. Basic information includes gender, age, pregnancy status, past medical history, past medication history, and drug allergy history.
[0041] The display module 120 is used to receive and display the predicted disease information and predicted disease probability value sent by the server 200 .
[0042] The server 200 includes a data processing module 210 , a data storage module 220 and an XGBoost disease prediction module 230 .
[0043] The data processing module 210 is used to obtain the basic ...
Embodiment 2
[0078] Corresponding to the above-mentioned embodiment 1, this embodiment proposes an intelligent consultation method based on XGBoost disease prediction, which includes:
[0079] S100, the client terminal 100 obtains the patient's basic information and symptom information by simulating the question-and-answer interaction between the doctor and the patient;
[0080] S200, the server 200 obtains the basic patient information and symptom information data of the client 100 for processing;
[0081] S300, the server 200 inputs the processed patient symptom information data into the trained XGBoost multi-classification model, outputs the predicted value of the disease probability and the predicted disease information, and sends them to the client 100;
[0082] S400. The client 100 receives and displays the predicted disease information and predicted disease probability value sent by the server 200 .
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