Medical disease prediction has become an important area of research due to the rapid growth of healthcare data and the need for early diagnosis. Machine learning techniques, especially K-Nearest Neighbors (KNN), are widely used for classification tasks in healthcare.This paper reviews the application of KNN in predicting various diseases such as diabetes, heart disease, and cancer.

KNN is a simple yet powerful algorithm that works based on similarity measures between data points. The study highlights how KNN handles medical datasets with multiple attributes and assists in decision-making. It also discusses the advantages of KNN such as simplicity, non-parametric nature, and adaptability to different datasets.

However, limitations like computational complexity and sensitivity to noise are also considered. Various research works have shown that KNN provides competitive accuracy compared to other algorithms. The paper summarizes key findings from previous studies and evaluates performance metrics.

It also explores improvements such as weighted KNN and feature selection techniques. Overall, the review concludes that KNN is an effective approach for medical disease prediction when applied appropriately.