Multi-Disease Prediction Using Machine Learning Algorithm

Abstract

Abstract: In the medical sector, disease diagnosis is an essential duty, and prompt and accurate diagnosis is crucial to effective management and therapy. Machine learning techniques, including Naive Bayesian networks, have shown promise in disease prediction and diagnosis. In this study, we present a machine learning-based multi-disease prediction system that uses Naive Bayesian networks. The proposed methodology seeks to deliver precise illness prediction for several diseases instantaneously. In addition to describing the methods adopted, which included dataset selection, preprocessing, feature selection, and the Naive Bayesian network algorithm, we also discuss the social relevance of this work, emphasizing the potential impact of accurate disease prediction in improving patient outcomes and bringing down healthcare costs. To evaluate the performance of the proposed model, we conducted experiments using a publicly available disease dataset. The results demonstrated that the proposed model achieved high accuracy of 91.2% and outperformed other state-of-the-art models for multi-disease prediction some of them are, Random Forest obtained 85.7% and Decision Tree obtained 81.3% respectively. In summary, the proposed system demonstrates the effectiveness of Naive Bayesian networks for multi-disease prediction and has the potential to improve disease diagnosis and management in the medical domain