Heart Attacks and Brain Strokes in Early, Mid and Late Age: Study From Data Science Perspective
Keywords:
Data Science, Heart Attack, Brain Stroke, Mid Age, Early AgeAbstract
Heart attacks and brain strokes have become major global health concerns due to the increasing number of cases occurring among not only older adults but also middle-aged and younger populations. Unhealthy lifestyles, smoking habits, obesity, hypertension, and diabetes have significantly contributed to the rising risk of cardiovascular and neurological diseases. This study aimed to investigate heart attack and brain stroke risks across different age groups from a data science perspective while evaluating the effectiveness of machine learning techniques for disease prediction. The research employed quantitative experimental analysis using healthcare datasets containing clinical and lifestyle-related variables such as age, blood pressure, cholesterol level, glucose level, smoking behavior, body mass index, and diabetes condition. Two supervised machine learning algorithms, namely Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), were implemented to classify disease risks. The experimental results showed that the SVM model achieved superior predictive performance with an accuracy of 99.5%, while XGBoost achieved 94.3% accuracy. The novelty of this study lies in integrating multi-age cardiovascular and neurological risk analysis with artificial intelligence-based predictive modeling to support the development of an intelligent mobile healthcare alert system. The study concluded that machine learning technologies have strong potential to improve early disease prediction, preventive healthcare, and proactive patient monitoring in modern healthcare environments.
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Copyright (c) 2026 Ankur Singh Bist, Untung Rahardja, Ninda Lutfiani, Rifqa Nabila Muti, Santiago Ramirez
This work is licensed under a Creative Commons Attribution 4.0 International License.
