Project Abstract

Heart disease is a widespread health concern globally, underscoring the importance of early detection methods for potential heart attacks. This research project delves into the utilization of advanced machine learning algorithms to aid in predicting individuals who may be at risk of experiencing a heart attack. By leveraging these algorithms, which analyse a variety of factors including age, gender, and health data, we aim to uncover patterns indicative of potential heart issues. The primary objective of this research is to develop a specialized computer program capable of accurately detecting signs of a heart attack by scrutinizing diverse health information. Through a meticulous comparison of different machine learning algorithms, we endeavour to identify the most effective approach for predicting heart problems. Our goal is to provide healthcare professionals with a valuable tool to facilitate early identification and intervention for individuals susceptible to heart issues. The methodology employed in this research is straightforward: we gather health data from various sources and subject it to analysis using sophisticated computer programs. Subsequently, we assess the performance of these programs to ascertain which one yields the most reliable predictions regarding heart problems. Our findings hold significant potential to enhance medical decision-making processes and elevate the standard of care for individuals with heart conditions. Ultimately, this research seeks to advance the field of cardiac care and contribute to improved health outcomes on a global scale by empowering healthcare providers with innovative tools for early intervention and prevention strategies.

Keywords: Heart Attack Detection, Predictive Modelling, Machine Learning Algorithms

 Conference Details

Session: Presentation Stream 7 at Presentation Slot 9

Location: GH018 at Tuesday 7th 13:30 – 17:00

Markers: Sofya Lyakhova, Troy Astarte

Course: MSc Advanced Computer Science, Masters PG

Future Plans: I’m looking for work