Advanced Predictive Analytics for Heart Disease using Deep Learning

Advanced Predictive Analytics for Heart Disease using Deep Learning

Jaydeep Dabhi CSE Department Parul University Vadodara, India

jaydeepdabhi51@gmail.com

Prof. Kishori Shekokar CSE Department Parul University Vadodara, India

kishori.shekokar20174@paruluniversity.ac.in

Prof. Mukesh Kumar CSE Department Parul University Vadodara, India

mukesh.manit86@gmail.com

Abstract—Heart disease remains the leading cause of death globally, accounting for approximately 17.3 million deaths an- nually. This highlights the critical need for effective strategies to address cardiovascular diseases, which significantly impact public health and impose a substantial economic burden. While high-income countries generally report lower prevalence due to advanced healthcare systems, low- and middle-income countries face a higher burden due to limited healthcare access and rising risk factors.

The healthcare industry generates vast amounts of data, making it ’information rich’ but often ’knowledge poor.’ This data includes electronic health records, medical imaging, and information from wearable devices. However, the challenge lies in extracting actionable insights due to data fragmentation across different systems.

Data mining techniques help uncover hidden patterns within this data, enhancing diagnostic accuracy and treatment efficiency. For instance, pattern recognition can identify early signs of heart disease, while predictive analytics can forecast health risks, enabling proactive interventions. Data mining also supports personalized medicine by tailoring treatment plans to individual profiles.

Visuals such as a world map of heart disease statistics and a diagram of healthcare data volume can effectively illustrate these insights. Despite the potential, challenges like data privacy, quality, and integration persist. Advancements in AI and machine learning are expected to enhance data analysis, offering new opportunities to improve patient care.