Machine Learning-Based Multiple Disease Forecaster

Abstract

Abstract: Accurate prediction of multiple diseases, including diabetes, chronic kidney disease, heart diseases, Parkinson's disease, and breast cancer plays a crucial role in proactive healthcare management and early intervention. In this study, we propose a machine learning-based multiple disease forecaster that leverages advanced algorithms to predict the likelihood of various diseases simultaneously. The forecaster utilizes a comprehensive dataset comprising patient demographics, medical history, and relevant clinical attributes. Feature engineering techniques are employed to extract informative features, which are then input into a diverse set of machine learning models, including Random Forest, Xgboost, and Support Vector Machines. The models are trained and fine-tuned using a dataset collected from kaggle. Performance evaluation is conducted using various metrics, including accuracy, precision, recall, and F1-score, to assess the predictive capability of the forecaster. The proposed forecaster holds promise for improving disease prediction accuracy, facilitating early intervention, and enhancing healthcare outcomes on a broader scale. Future research directions include incorporating additional data sources such as genetic information and exploring interpretability techniques to gain insights into the underlying disease mechanisms