A Novel XGBoost-Based Ensemble Framework for Disease Prediction and Drug Recommendation in Healthcare

Abstract

Machine learning has the potential to transform healthcare by predicting diseases and recommending drugs based on the symptoms. These systems have the potential to transform patient care by utilizing advanced algorithms, large datasets, and interdisciplinary collaboration. In this research work, we focus on the ideas and potential outcomes of using machine learning techniques for disease prediction and treatment recommendations. In this research paper we use machine learning (ML) techniques where patients can quickly find out about the illness and the medication that can help treat it by simply describing their symptoms they are experiencing. In this study we use XGBoost ensemble method for disease prediction and drug recommendation based on symptoms and also do some comparative study with other techniques such as Random Forest, Decision Tree and SVM and found that the accuracy of XGBoost is outperforming than other mentioned techniques.