Enhancing Healthcare Precision: Utilizing Machine Learning for Accurate Patient Age Group Prediction

Because machine learning (ML) can forecast age properly, it presents a substantial advantage in the dynamic field of healthcare when it comes to medical research, personalized therapies, and public health management. This paper uses a full machine learning technique to predict the patient age groups. It makes use of a dataset that includes a variety of attributes, including blood glucose, gender, physical activity, BMI, diabetes status, and insulin levels. Several supervised learning algorithms were applied, including Decision Tree, Random Forest, XGBoost, SVM, Logistic Regression, and KNN. Maximum accuracy for the XGBoost algorithm was 97.2%, while for Random Forest, it was 94.2%. The findings show that machine learning significantly increases the accuracy of age prediction, supporting tailored treatments and preventative health care actions. By utilizing data-driven insights, this finding marks a paradigm change in precision medicine, transforming the delivery of health care and encouraging a more health-conscious population.