[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123131-en":3,"doc-seo-123131-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123131,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",7,"Healthcare","Chronic disease prediction chatbot using deep learning and machine learning algorithms","This paper presents a disease prediction framework that combines deep learning and classical machine learning with a text-based assistant chatbot. A predictive model is trained to infer potential diseases from user symptoms and is implemented using KNN, SVM, random forest, and neural network approaches, with LSTM used in the chatbot application. The system integrates NLTK libraries and the Telegram platform to guide user input and conversation flow. Results show SVM reaching 92.24% accuracy, with random forest at 92.23% and KNN and ANN slightly lower.","Chronic disease prediction chatbot using deep learning and machine learning algorithms  \nMandy Sia1, Kok-Why Ng1, Su-Cheng Haw1, Jayapradha Jayaram2  \n1Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia 2Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, India  \nArticle history:  \nReceived Mar 12, 2024 Revised Aug 27, 2024 Accepted Oct 17, 2024  \nKeywords:  \nChatbot  \nDisease prediction Long short-term memory Machine learning Neural network  \nCorresponding Author:  \nEver 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.  \nThis is an open access article under the CC BY-SA license.  \nKok-Why Ng  \nFaculty of Computing and Informatics, Multimedia University Jalan Multimedia, 63100 Cyberjaya, Malaysia [Email: kwng@mmu.edu.my](Email: kwng@mmu.edu.my)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe world today has been on high alert for public health hazards due to the COVID-19 outbreak since the end of 2019. Worse still in July 2022, a second public health threat, Monkeypox, was declared a worldwide health epidemic while the COVID-19 pandemic is still raging. These successive public health threats have put immense strain on a population still recovering from the initial impact. However, these crises have also heightened public awareness regarding health issues, particularly the importance of early symptom detection for timely treatment [1], [2] . Yet, the vast number of diseases and their associated symptoms make it impractical for individuals to memorize them accurately. This creates a significant challenge in analyzing potential illnesses based on specific symptoms.  \nFortunately, machine learning techniques offer a promising solution [3]-[5] . These techniques can accurately predict diseases based on black-and-white data. Machine learning comes at no cost and is less influenced by personal judgments. The machine learning model will act like the brain of a physician while the chatbot acts as the bridge to communicate with the patient [6]-[8] . A chatbot can be handy in collecting  \nthe necessary inputs and guiding the conversation in the right direction [9]-[12] . Therefore, by combining the disease prediction model with the chatbot, people can reach out for help from this disease prediction assistant to identify their possible conditions for free.  \nSome of the well-known machine learning techniques are decision tree, random forest, enseble learning, boosted trees, and so on [13], [14] . As decision tree can predict using a predefined classification tree that combines both numerical and categorical features, it can yield high average accuracies. 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