ITDS | SPMS

CORONARY AND HEART ATTACK DISEASE PREDICTION USING DEEP LEARNING (CNN)

Project Description
Supervisor

Heart disease is a primary killer in the world. The world health organization or WHO estimates approximately 17.9 million people caused by the heart-related death. Machine learning algorithms on structured textual data are capable of enhancing the rightfulness of prediction, which can guide medical experts in their decision-making activity, whereas a range of technologies has been developed to make early detection and diagnosis possible. In text-based classification, the use of patient records which integrate medical features and observations is very crucial in the determination of trends in clinical data. This paper proposes a deep learning method of predicting heart disease using a textual dataset. A Deep Learning, Convolutional Neural Network (CNN) model was specifically trained and developed using the University of California Irvine (UCI) heart disease which contains 1,050 patient records and 14 clinical variables such as age; cholesterol level; resting blood pressure; type of chest discomfort etc. The performance of the model was evaluated using standard metrics which include accuracy, precision, recall and F1-score. Since the proposed model received validation accuracy of 91.7 percent, it was shown to be effective in finding the risk of heart disease based on structured textual information. Such results emphasize the potential of deep learning in the field of medicine diagnostics, particularly regarding the use of such solutions within a network of patient data received in clinical practice.

Artificial Intelligence: Artificial Intelligence (AI) research in Computer Science and Information Technology explores the development of systems and algorithms that can perform tasks typically requiring human intelligence. These tasks include problem-solving, decision-making, learning, perception, and language understanding. AI research is a vast and interdisciplinary field, encompassing a variety of areas such as machine learning, natural language processing, computer vision, robotics, and cognitive computing. Machine learning (ML) is a core component of AI research. It focuses on creating algorithms that allow computers to learn from data, improve over time, and make predictions or decisions without explicit programming. Subfields of ML include supervised learning, unsupervised learning, reinforcement learning, and deep learning, with applications ranging from image recognition to recommendation systems. Natural language processing (NLP) is another critical area within AI research. It aims to enable machines to understand, interpret, and respond to human language. Researchers in NLP work on tasks like speech recognition, language translation, and sentiment analysis, making it possible for computers to interact with humans in a more natural and intuitive way.

Creating a system to predict heart disease is the primary objective of this project. The heart disease prediction system seeks to improve the accuracy of heart disease prediction by applying deep learning techniques to medical data sets.

2024/2025

Oct 16, 2025

TANLEY BITUDO CLEMENT AGYEMAN KOFI TAKYI AMPONSAH GYAMAA BENEDICTA ASANTE ANTHONY

BSc Information Technology