Authors: Ashwani Kumar, Dr. Sunil Maggu
Abstract: The intersection of machine learning and preventive healthcare offers transformative potential for earlydisease detection and personalized health guidance. However, most existing systems either producebinary classification outcomes without contextual risk stratification or provide static, non-adaptivehealthrecommendations disconnected from individual prediction confidence scores. This paper introducesHealthGuard AI, a novel multi-stage predictive framework that integrates supervised machinelearningclassification with probability-based risk stratification and a dynamically adaptivehealthrecommendation engine. The system simultaneously addresses three major chronic diseasedomains—Type 2 Diabetes Mellitus, Coronary Heart Disease, and Parkinson’s Disease — using clinicallyvalidatedfeature sets drawn from UCI Machine Learning Repository datasets. Beyond binary prediction, HealthGuard AI applies predict_proba() outputs to stratify individual disease risk into Low(≤0.40), Medium (0.41–0.70), and High (> 0.70) categories, each triggering a distinct, evidence-alignedhealthrecommendation profile. An additional Body Mass Index and Basal Metabolic Rate estimationmoduleemploying the Mifflin-St Jeor equation further extends the system’s scope into nutritional healthanalytics. Deployed as an interactive web application via Streamlit Community Cloud, HealthGuardAIachieves classification accuracies of 78.5%, 81.3%, and 87.2% for Diabetes, Heart Disease, andParkinson’s Disease respectively. The system demonstrates that probability-aware risk stratification, when combined with adaptive, risk-tiered recommendations, produces a meaningfully richer andmoreclinically actionable output than conventional binary prediction pipelines. Experimental results, systemarchitecture, and the clinical relevance of risk-tiered adaptive recommendations are discussedindetail.
DOI: https://doi.org/10.5281/zenodo.19993977
