ITDS | SPMS

USING MACHINE LEARNING ALGORTHIMS TO PREDICT MALARIA AND HEPATITIS B

Project Description
Supervisor

This project develops a Disease Prediction System using machine learning to predict malaria and hepatitis B based on patient symptoms. By analyzing patterns in symptoms and historical medical data, the system aims to improve diagnostic accuracy and support timely interventions, particularly in areas where these diseases are prevalent. The system's features include disease prediction, comprehensive descriptions, diet, precautions and workout. Utilizing advanced machine learning models such as Random Forest, Gradient Boosting Classifier, MultinomialNB and Support Vector Classifier, the system processes complex medical data to deliver reliable and personalized healthcare solutions. By addressing the limitations of traditional diagnostic methods, which can be time-consuming and resource-intensive, this project contributes to reducing the burden on healthcare providers and empowers patients with actionable insights. The implementation of this system underscores the transformative potential of AI in healthcare, particularly in enhancing the predictive diagnosis of critical diseases like malaria and hepatitis B.

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 a machine learning model that accurately predicts malaria and hepatitis B based on patient-reported symptoms.

2023/2024

Feb 12, 2026

EBENEZER DAMPTEY (UEB3213920), DERRICK DONKOR (UEB3270122), FELIX DONGBETIGR (UEB3211220), AMPONSAH LUCAS (UEB3220020), SUNDONG WISDOM NATONAAH (UEB3245522), ASARE NANA KWASI (UEB3266622)

BSc Information Technology