Classification Model for Meticulous Presaging Heart disease Results for sarcopenic LRMM and CA calcification in healthy adults using Machine learning

Sarcopenic obesity, a condition characterized by both muscle loss and excess body fat, is a significant risk factor for cardiovascular disease. This study aims to develop a machine learning model to predict the risk of sarcopenic obesity and heart disease. A dataset comprising 3050 patients aged 66 years and older was obtained from the UC Irvine Machine Learning Repository. The dataset includes relevant attributes such as BMI, protein intake, blood urea nitrogen (BUN), fat percentage, age, complete blood count (CBC), chest pain, sex, and more. Several machine learning algorithms, including Support Vector Machine, Naïve Bayes, Decision Tree, and Logistic Regression, were employed to build the prediction model. The model’s performance was evaluated based on its accuracy in identifying individuals at risk of sarcopenic obesity and heart disease. The results of this study demonstrate the potential of machine learning in early disease detection and prevention. By accurately predicting the risk of sarcopenic obesity and heart disease, healthcare providers can implement timely interventions and improve patient outcomes..