Citation

Sia, Mandy and Ng, Kok Why and Haw, Su Cheng and Jayaram, Jayapradha (2025) Chronic disease prediction chatbot using deep learning and machine learning algorithms. Bulletin of Electrical Engineering and Informatics, 14 (1). pp. 742-751. ISSN 2089-3191

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Abstract

Ever since the rise of human civilization, more and more diseases have been discovered with the rapid growth of medical knowledge. This sheer volume of information makes it hard for humans to memorize or even utilize it efficiently. Thus, machine learning emerged as a powerful tool for complex calculations by offering a solution to this challenge. This paper intends to use deep learning and machine learning algorithms to develop a predictive model that can recognize potential diseases based on symptoms. The model is then seamlessly integrated into a text-based disease prediction assistant chatbot that serves as a communication platform between the users and the system. The algorithms researched for the disease prediction models are knearest neighbours (KNN), support vector machines (SVM), random forest, and neural networks. After that, a chatbot application is created by integrating long short-term memory (LSTM), natural language toolkit (NLTK) libraries, and Telegram. As a result, the SVM models demonstrated excellent performance by achieving an accuracy of 92.24%, closely followed by random forest with 92.23%, KNN with 91.57%, and artificial neural network (ANN) with 91.52% accuracy. In short, this paper presents a potential solution for a more accurate disease prediction tool by implementing the best disease prediction model with the chatbot models together.

Item Type:	Article
Uncontrolled Keywords:	Chatbot, disease prediction, long short-term memory, machine learning, neural network
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	18 Feb 2025 03:47
Last Modified:	18 Feb 2025 06:01
URII:	http://shdl.mmu.edu.my/id/eprint/13478

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