Survey Paper on A Machine Learning Approach for Multiple Disease Prediction

Abstract

Machine learning techniques have revolutionized the field of healthcare by enabling accurate and timely disease prediction. The ability to predict multiple diseases simultaneously can significantly improve early diagnosis and treatment, leading to better patient outcomes and reduced healthcare costs. This research paper explores the application of machine learning algorithms in predicting multiple diseases, focusing on their benefits, challenges, and future directions. We present an overview of various machine learning models and data sources commonly used for disease prediction. Additionally, we discuss the importance of feature selection, model evaluation, and the integration of multiple data modalities for enhanced disease prediction. The research findings highlight the potential of machine learning in multi- disease prediction and its potential impact on public health. Once more, I am applying machine learning model to identify that a person is affected with few disease or not. This training model takes a sample data and train itself for predicting disease. Key Words: Disease Prediction, Na ̈ıve Bayesian Networks, Random Forest, Decision Tree, Feature , Selection KNN.