Disease Prediction Algorithm Using Machine Learning: A Review

In recent years, the advent of machine learning (ML) has revolutionized various sectors, including healthcare, by enabling predictive analytics for disease diagnosis and prognosis. The implementation of disease prediction algorithms utilizing machine learning techniques such as decision trees, naive Bayes, and LIME model has emerged as a pivotal area in healthcare research. This review paper provides a comprehensive analysis of the recent advancements in this domain, focusing on the development, evaluation, and comparative assessment of these algorithms. By synthesizing existing literature, key methodologies, and findings are examined, elucidating the strengths and limitations of each approach. Furthermore, the paper discusses the significance of feature selection, model interpretability, and data preprocessing techniques in enhancing prediction accuracy and clinical applicability. Strategies for mitigating algorithmic biases and optimizing model performance are also addressed. Through a critical synthesis of empirical studies, this review offers insights into the current state-of-the-art techniques, identifies research gaps, and provides recommendations for future directions in the implementation of disease prediction algorithms using machine learning.