Predicting Lung Disease Using Machine Learning: A Comparative Study of Logistic Regression, k-Nearest Neighbors, and Naive Bayes

Abstract

Machine Learning (ML) has emerged as a transformative technology across various domains, revolutionizing industries such as healthcare, finance, and manufacturing. This paper provides a comprehensive review of recent advancements in ML, focusing on key algorithms, applications, and challenges. Specifically, it explores the use of common ML algorithms—Logistic Regression (LR), k-Nearest Neighbors (KNN), and Naive Bayes (NB)—for predicting lung disease based on a dataset containing 1,000 instances. The study demonstrates how these algorithms are applied to classify lung disease cases, highlighting their effectiveness and comparing performance metrics. Despite the advancements, challenges such as the need for large labeled datasets, model interpretability, computational complexity, and biases in predictions remain. The paper also discusses future research directions, including few-shot learning, explainable AI, transfer learning, edge computing, and ethical AI, which aim to address current limitations and unlock new potentials for ML in healthcare and beyond.