Medicine Recommendation System

Project Abstract

The Medicine Recommendation System (MRS) is a health/disease prediction mobile app using Artificial Intelligence to look at appropriate medicine regarding user syndromes. The system can predict disease, symptom information, and recommended medication using machine learning algorithms and historical medical datasets. First, we developed a process for data collection, and pre-processing before model training and then system testing. Other challenges included integrating data and preserving data privacy. Still, future development efforts will be around increasing the robustness of models through ensuring security as well as improving its core forecasts. The app is a resource for patients and healthcare providers that is intended to facilitate more educated clinical decision-making and better patient care.

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

• To create a machine learning model that predicts diseases based on symptoms reported by patients.

Academic Year

2023/2024

Date Uploaded

Nov 18, 2024

Group Members

AGYEI SAMUEL (UEB3221320) NTI GYIMAH EMMANUEL (UEB3200120) PRUDENCE AKOSUA ADJAKLO (UEB3215220) ANANE BUSIA LAWRENCE (UEB3221520) GYABAAH ABRAHAM (UEB3207620)

Program Of Study

BSc Information Technology