AN ENSEMBLE MODEL FOR MULTIPLE DISEASE PREDICTION USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES

Abstract

Multiple Disease Prediction using Machine Learning, Deep Learning is a comprehensive project aimed at predicting various diseases including diabetes, heart disease, kidney disease, Parkinson's disease, and breast cancer. This project leverages machine learning algorithms such as TensorFlow with Keras, Support Vector Machine (SVM), Logistic Regression and Proposed Hybrid algorithm. The models are deployed using Streamlit Cloud and the Streamlit library, providing a user-friendly interface for disease prediction. The primary goal is to develop a comprehensive system that can predict the likelihood of multiple diseases based on patient data. By using both machine learning (ML) and deep learning (DL) methods, the system can be designed to analyze various types of medical information such as clinical data to detect early signs of disease, provide accurate diagnoses, or even forecast the progression of these conditions. Once the models are trained, they must be evaluated for their predictive performance using metrics such as accuracy, precision, recall and F1-score. This helps in determining how well the model can predict various diseases. Cross-validation techniques can be applied to ensure that the model generalizes well across different patient populations. The research focuses on building an intelligent, multi-disease prediction system by combining traditional machine learning models with deep learning approaches for handling different types of medical data. The research holds immense potential in improving personalized healthcare and reducing the burden of chronic diseases worldwide. The high accuracies achieved by the different models demonstrate the effectiveness of the employed machine learning algorithms in disease prediction.