Heart Disease Risk- Key Health Indicators, 10/23, Springfield, Illinois, Evaluated predictive modeling by neural network with 3 hidden units, achieving a remarkable 75.72% accuracy rate in forecasting heart disease risk based on vital health factors such as age, BMI, physical activity, and family history best predictive model, Synthesized dataset of NHANES, CDC via Kaggle by data cleaning, data mining, data modifications in SAS Miner to reduce data size from 319,796 to 18,249 values and create variables like age_category, heart disease, Computed statistical/ predictive analysis using ward clustering, average, centroid clustering methods, market segmentation, customer characterization, classification trees (ClassDecTree B2D6 with MISC rate of train), logistic regression, neutral networks, Reported 5 data visualization graphs via Tableau 2023 to determine relation between heart disease, average BMI for males, females