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Human Disease Prediction

Developed by AWeirdDev
A tabular classification model based on scikit-learn for predicting possible diseases based on symptoms.
Downloads 86
Release Time : 2/12/2024

Model Overview

This model predicts possible disease types by analyzing the symptom feature vector (0/1 indicates the presence or absence of symptoms) input by the user. It is suitable for medical auxiliary diagnosis scenarios.

Model Features

Symptom feature analysis
Supports binary input of 132 symptoms, comprehensively covering common disease manifestations
Efficient prediction
Based on a lightweight scikit-learn model, it can achieve rapid disease prediction
Simple integration
Provides a standardized joblib model interface, easy to integrate into existing systems

Model Capabilities

Symptom feature analysis
Disease probability prediction
Medical auxiliary diagnosis

Use Cases

Medical and health
Symptom self-check tool
Patients obtain possible disease predictions after inputting symptoms
Provide preliminary diagnosis reference (to be confirmed by professional doctors)
Triage system
Assist medical institutions in preliminary symptom classification
Improve triage efficiency
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