ITDS | SPMS

DESIGN AND IMPLEMENTATION OF A HEART FAILURE DETECTION SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS

Project Description
Supervisor

Heart failure is a critical and widespread cardiovascular condition characterized by the heart's inability to pump blood effectively, leading to serious health complications and increased mortality rates. Early detection and timely intervention are essential for improving patient outcomes and reducing healthcare costs. Traditional diagnostic approaches often involve a series of labor-intensive tests and require specialized medical expertise, which can delay diagnosis and treatment. In this study, we propose a novel smartphone-based heart failure prediction system that leverages convolutional neural networks algorithm to automate and enhance the accuracy of heart failure risk assessments. The system utilizes a comprehensive dataset to train and evaluate various the model. The system's performance is evaluated using metrics such as accuracy and Loss. The results indicate that model achieved high accuracy and Loss, with an accuracy of 9%, Loss of 92%. These outcomes demonstrate the potential of machine learning models in providing reliable and timely predictions of heart failure. The developed smartphone application represents a significant innovation in the field of digital health. It offers a user-friendly, accessible platform for real-time heart failure prediction, enabling patients to monitor their risk and seek medical attention proactively. This system is expected to streamline the diagnostic process, reduce healthcare costs, and improve patient outcomes by facilitating earlier and more accurate detection of heart failure compared to traditional methods. In summary, our project contributes to the advancement of healthcare technology by integrating machine learning into a practical tool for heart failure prediction, with the potential to transform patient care and clinical practices.

Machine Learning: Machine Learning (ML) research in Computer Science and Information Technology focuses on the development of algorithms and models that enable computers to learn from data and improve their performance over time without being explicitly programmed. It is a subset of Artificial Intelligence that uses statistical techniques to give machines the ability to learn patterns, make decisions, and predict outcomes based on data. Supervised learning, a key area of ML research, involves training models on labeled data, where the input-output relationships are predefined. This method is widely used for tasks such as classification (e.g., spam detection) and regression (e.g., predicting house prices). Unsupervised learning, on the other hand, involves finding hidden patterns in data without predefined labels, with clustering and association being typical applications in areas such as customer segmentation and anomaly detection. Reinforcement learning is another area of ML that focuses on teaching agents to make decisions by interacting with their environment and receiving feedback in the form of rewards or penalties. It is often applied in robotics, game playing, and autonomous systems, where continuous learning and adaptation are required.

To develop an Android application that uses Convolutional neural networks to detect heart failure.

2023/2024

Feb 12, 2026

FORDJOUR HESKEY AMOAKO (UEB3202120), OPOKU BENEDICT (UEB3208420), ASIEDU YEBOAH RICHMOND (UEB3269422), GYAN KELVIN ISAAC (UEB3209020), OSEI GIDEON (UEB3202620)

BSc Information Technology