Disease prediction web application using machine learning
In recent years, the prevalence of diseases has increased and the demand for quick diagnosis tools is growing. This has highlighted the need for machine learning-based web applications for disease predictions is important in the healthcare system for early diagnosis. This project presents the design...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Online Access: | http://eprints.utar.edu.my/7289/1/SE_2105105_FYP_Report%2DFooJiaYu_FOO_JIA_YU_1.pdf http://eprints.utar.edu.my/7289/ |
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| Summary: | In recent years, the prevalence of diseases has increased and the demand for quick diagnosis tools is growing. This has highlighted the need for machine learning-based web applications for disease predictions is important in the healthcare system for early diagnosis. This project presents the design and development of a web-based disease prediction application that employs machine learning and natural language processing technologies to assist users in identifying potential health conditions. The motivation for this project is to improving access to early diagnosis, reduce the burden on medical staff and getting general medical advice anywhere and anytime. The methodology involved develop and train machine learning models on Symptom-Disease Prediction Dataset (SDPD) to achieve precise predictions, integrate the model into web application built on Flask and React, and employ Google Gemini to generate general medical recommendations and extract symptoms. System testing was conducted through multiple testing methods, including unit testing, integration testing, user acceptance testing (UAT) and user interface design feedback collected through Google Forms. The results indicate that the machine learning model achieved a prediction accuracy at approximately 97%. User acceptance testing validated that over 90% of users rated the usability and ease of use of the system at 4 or higher on a 5-point Likert scale. The study concluded that the system successfully achieved its objectives, delivering a practical, user-friendly, and intelligent healthcare support system. However, it also acknowledged limitations such as dependence on dataset quality, lack of coverage for rare or new diseases, and multilingual support. Future work will focus on expanding dataset variety, integrating multilingual support, and incorporating of contextual health data to further enhance prediction accuracy and precision.
Keywords: Disease prediction, Machine Learning, Web Application, Large Language model, Natural Language Processing
Subject Area: QA76 – Computer Science |
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