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I also declare that it has not been previously and concurrently submitted for any other degree or award at UTAR or other institutions.  \nName :  FOO JIA YU   \nID No. :  2105105   \nDate :  01/07/2025   \nCOPYRIGHT STATEMENT  \n© 2025, FOO JIA YU. All right reserved.  \nThis final year project report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Software Engineering (Honours) at Universiti Tunku Abdul Rahman (UTAR) . This final year project report represents the work of the author, except where due acknowledgement has been made in the text. No part of this final year project report may be reproduced, stored, or transmitted in any form or by any means, whether electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the author or UTAR, in accordance with UTAR’s Intellectual Property Policy.  \nACKNOWLEDGEMENTS  \nFirst and foremost, I would like to express my gratitude to Dr. Fatimah Audah binti Md. Zaki as my research supervisor and Mr Kelwin Tan Seen Tiong as my research moderator for their invaluable advice, guidance, enormous patience and constructive feedback throughout the duration of the research.  \nI sincerely thank my academic advisor, Dr. Khor Kok Chin, for his exceptional guidance and support throughout my degree program. I would also like to thank all the lecturers and the faculty and the departmental members from Lee Kong Chian Faculty of Engineering and Science for creating a pleasant working environment throughout my years in UTAR.  \nSpecial thanks go to my fellow classmates and friends for their collaboration, discussions, and encouragement during challenging times. Their support helped me kept motivation and progress steadily.  \nFinally, I am deeply grateful to my family for their unconditional love, patience, and unwavering trust. Their encouragement has been my greatest source of strength and inspiration throughout my academic journey.  \nABSTRACT  \nIn 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 th","cbCaik6tu9BBckYO","https://ap.wps.com/l/cbCaik6tu9BBckYO","pdf",6702558,221,"English","# CHAPTER 1 INTRODUCTION\n## 1.1 General Introduction\n## 1.2 Importance of the Study\n## 1.3 Problem Statement\n## 1.3.1 Long waiting times for patients to get diagnosis\n## 1.3.2 Increased Risk of Human Errors\n## 1.3.3 Limited accessibility for remote areas\n## 1.4 Aim and Objectives\n## 1.5 Scope and Limitation of the Study\n## 1.5.1 Limitation\n## 1.6 Proposed Solution\n## 1.7 Project Approach\n# CHAPTER 2 LITERATURE REVIEW\n## 2.1 Introduction\n## 2.2 Research and Comparison Model\n## 2.2.1 Research Models\n## 2.2.2 Compare Existing Models\n## 2.2.3 Summary of Comparative Models\n## 2.3 Compare Existing Web App\n## 2.3.1 Symptomate\n## 2.3.2 WebMD Symptom Checker\n## 2.3.3 Your.MD (Healthily)\n## 2.3.4 Summary of Existing Applications\n## 2.4 Evaluation Metrics\n## 2.4.1 Accuracy\n## 2.4.2 Sensitivity (Recall)\n## 2.4.3 Specificity\n## 2.4.4 Precision\n## 2.4.5 F1-Score (F-Measure)\n## 2.4.6 Summary of Evaluation Metrics\n## 2.5 Dataset Sources\n## 2.5.1 Summary of Data Source\n## 2.6 Web Application Framework\n## 2.6.1 Backend Framework\n## 2.6.2 Frontend Framework\n## 2.7 Summary\n# CHAPTER 3 METHODOLOGY AND WORK PLAN\n## 3.1 Introducti","[{\"question\":\"What problem does the disease prediction web application aim to address?\",\"answer\":\"It targets the growing need for quick diagnosis support by enabling early disease prediction from user-provided symptoms and providing general medical recommendations.\"},{\"question\":\"Which datasets and technologies are used in the project?\",\"answer\":\"The machine learning models are developed and trained using the Symptom-Disease Prediction Dataset (SDPD), integrated into a Flask and React web application, and enhanced with Google Gemini for recommendations and symptom extraction.\"},{\"question\":\"How were the system results evaluated and what performance was achieved?\",\"answer\":\"The project used unit testing, integration testing, and user acceptance testing (UAT) plus UI feedback collected via Google Forms. The model achieved about 97% prediction accuracy, and over 90% of users rated usability and ease of use at 4 or higher on a 5-point Likert scale.\"}]","DISEASE PREDICTION WEB APPLICATION USING MACHINE LEARNING - Project Report | PDF",1789641253,557]