HEART INTEL: AI-DRIVEN HEART DISEASE PREDICTION

Project Abstract

Heart disease remains one of the leading causes of death globally, presenting a critical challenge to healthcare professionals. Despite advances in traditional diagnostic methods, the need for a more accurate and timely detection system is paramount. This work employed five machine learning algorithms namely Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest, and Decision Trees to predict the presence of heart disease, using a range of parameters (Accuracy, Precision, Recall, F1-Score). The dataset, sourced from Kaggle, underwent comprehensive pre-processing, which included data cleaning, handling missing values, and normalizing continuous variables to ensure model accuracy. The dataset was split into 80% training and 20% testing. The results showed that the SVM model outperformed other algorithms, by achieving an accuracy of 95%, followed by KNN at 92%, Logistic Regression at 88%, Decision Trees at 85%, and Random Forest at 78%. By fine-tuning the hyperparameters of these models, we ensured optimal performance, making the SVM the most suitable for deployment in clinical settings. This project’s predictive model could enhance early detection, streamline diagnosis, and ultimately contribute to improved patient outcomes.

Research Area

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.

Project Main Objective

• The main objective of this work is to use Machine Learning algorithms; Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest, and Decision to predict the presence of heart disease.

Academic Year

2023/2024

Date Uploaded

Nov 1, 2024

Group Members

ASANTE KWABENA STEPHEN, BOATENG KWABUAH REIGNALINE, AHI FELICITY, POMAA ADWOA JACYNTHA, ABDUL HARRIS MOHAMMED

Program Of Study

BSc Information Technology