Predictive Analysis of Health and Lifestyle Patterns using CRM and Machine Learning

Abstract: This study shows how CRM and machine learning can be used to predict health and living trends in order to help with preventive healthcare. The study uses an open source dataset that has factors about demographics, physical exercise, food, sleep, mental health, and medical background. Data preparation was done to make the data better by dealing with missing values, getting rid of duplicate and incomplete records, lowering the number of errors, and getting the dataset ready for reliable model training. It was possible to turn clinical, physiological, and behavioral traits into useful data for machine learning models by using feature engineering and feature extraction. For figuring out health risks and predicting them, Random Forest, Decision Tree, Logistic Regression, XGBoost, and voting-based classification methods were looked at. With AUC values of 0.68 and 0.64, respectively, the ROC results showed that Random Forest did a little better than Decision Tree. The overall risk classification showed that 43.7 percent of people were low risk, 35.4% were intermediate risk, and 20.9 percent were high risk.

Keywords: Health and lifestyle patterns; Machine learning; Preventive healthcare; Random Forest; Decision Tree; Risk stratification.