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Exploring the realm of machine learning involves monitoring the health of elderly loved ones by tracking their motions to keep them healthy. Datasets created by recording the body movements of elderly people are input to machine learning models for prediction. In this study, the proposal is to compare two popular machine learning algorithms KNN and K-Means for parameters like accuracy and precision. The ageing population has become a significant concern worldwide, as it poses a significant challenge to healthcare systems. The deterioration of health in elderly individuals is multifactorial, and it is essential to develop predictive models to identify potential health risks and intervene early. This study aims to explore using KNN(K-Nearest Neighbours) and K Means algorithms to analyse the health data of elderly individuals. The study collected and analyzed data from a cohort of elderly individuals, including demographic, lifestyle, and clinical parameters. The KNN algorithm was used to predict the likelihood of developing chronic diseases, such as diabetes, hypertension, and cardiovascular diseases, based on the input features.