• Siddesh.A.S
• Dr.K.T.Veeramanju

This paper explores the changing landscape of cardiovascular diseases (CVDs), extending beyond aging populations to impact the youth due to modern lifestyle shifts. Leveraging big data analytics, the study delves into intricate factors influencing cardiac health among younger individuals, unravelling complexities related to sedentary lifestyles, dietary habits, and genetic predispositions. The evaluation of various machine learning classifiers for predicting heart disease reveals Logistic Regression and Random Forest as standout models, demonstrating high accuracy and balanced metrics. SVM and KNeighbors offer a well-balanced approach, while XGBoost showcases competitive precision. The proposed modified LightGBM presents a balanced alternative. Model selection depends on application priorities, emphasizing accurate predictions, effective capture of positive instances, or balanced metric performance. In summary this study contributes insights into constructing a robust prediction model tailored for addressing cardiac health issues in the younger demographic through machine learning methodologies. The findings, driven by big data analytics, offer transformative potential, reshaping our comprehension of cardiovascular health and guiding targeted strategies and preventative measures.

"A prediction model for addressing the cardiac health issue among young people using Big Data Analytics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.e463-e474, January-2024, Available :http://www.jetir.org/papers/JETIR2401456.pdf

"A prediction model for addressing the cardiac health issue among young people using Big Data Analytics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppe463-e474, January-2024, Available at : http://www.jetir.org/papers/JETIR2401456.pdf