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This is a web application that aims to help with the diagnosis of various diseases such as breast cancer, chronic kidney disease, coronary heart disease, liver disease, and diabetes mellitus by analyzing different parameters. The application employs machine learning models on the backend, which is a technique of integrating several ML models

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visheshgupta-BA/Predictive-Healthcare-Analytics-using-ML

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Predictive Healthcare Analytics using Machine Learning

This web application is designed to help with the diagnosis of various diseases such as breast cancer, chronic kidney disease, coronary heart disease, liver disease, and diabetes mellitus by analyzing different parameters. The application employs machine learning models on the backend, which is a technique of integrating several ML models.

Visit our web application at https://vishesh-gupta-diagnosis-labs.streamlit.app/

Home Page App

User Input Parameters

Diagnosis Status

Web Database

Features

  • Provides predictions for different diseases using machine learning models.
  • Analyzes various parameters to make accurate predictions.
  • Offers a user-friendly interface for easy navigation.
  • Provides images of each disease for better understanding.

Supported Diseases

  1. Breast Cancer
  2. Chronic Kidney Disease
  3. Coronary Heart Disease
  4. Liver Disease
  5. Diabetes Mellitus

How it works

The application analyzes different parameters provided by the user and uses them to make a prediction for the respective disease. The parameters for each disease are unique and the machine learning models are trained on data sets specific to each disease. The application then provides a result with a probability of occurrence of the respective disease.

Machine Learning Models

The following machine learning models are used in this project to provide accurate predictions:

  • Logistic Regression
  • Support Vector Machines (SVM)
  • Gradient Boosting
  • Random Forest
  • Decision Tree
  • K-Nearest Neighbors (KNN)
  • Naive Bayes
  • Extreme Gradient Boosting (XGBoost)

Each model is trained on specific data sets for the respective disease to ensure accurate predictions. These models are integrated on the backend of the application to provide predictions based on the parameters provided by the user.

Getting Started

To use this application, simply visit the website and input the required parameters for the respective disease. The application will provide a result with a probability of occurrence for the disease.

Visit our web application at https://vishesh-gupta-diagnosis-labs.streamlit.app/

About

This is a web application that aims to help with the diagnosis of various diseases such as breast cancer, chronic kidney disease, coronary heart disease, liver disease, and diabetes mellitus by analyzing different parameters. The application employs machine learning models on the backend, which is a technique of integrating several ML models

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