Abstract
This research paper investigates the creation and assessment of a health chatbot system that aims to enhance healthcare services using decision trees and support vector machines (SVM). The primary goal is to develop a chatbot that effectively aids users in analyzing symptoms and provides personalized healthcare recommendations. To achieve this, a comprehensive dataset is gathered, consisting of patient symptoms and corresponding diagnoses. Rigorous preprocessing techniques are applied to ensure the quality and usability of the dataset. The study employs decision trees and SVM as the machine learning algorithms. Decision trees construct a tree-like structure that enables the chatbot to analyze symptoms. By traversing the decision tree based on user inputs, the chatbot can identify the most likely diagnosis, facilitating accurate and efficient symptom analysis. On the other hand, SVM is utilized to generate tailored treatment recommendations. By examining patterns and relationships within the dataset, the SVM algorithm can provide personalized healthcare guidance to users. To evaluate the effectiveness of the proposed health chatbot system, a comprehensive set of experiments and performance metrics is used. Accuracy, precision, recall, and F1-score are employed to assess the diagnostic accuracy and effectiveness of the chatbot in providing suitable recommendations. The chatbot demonstrates a high level of performance, delivering valuable assistance in symptom analysis and providing appropriate healthcare guidance to users. These advancements open the door for more precise and personalized medical decision support systems, as well as the creation of chatbots for the healthcare industry. The findings underscore the effectiveness of decision trees and SVM in enhancing the capabilities of health chatbots. This research has significant implications for the future development and implementation of chatbot technology in healthcare, ultimately leading to improved healthcare services and outcomes for patients.
Keywords
Health Chatbot, Machine Learning, Medical Diagnosis, Disease Identification, Symptom Analysis, and Treatment Recommendations.
Citations
IRE Journals:
Bhavika Jain , Dr. Sunil Maggu
"Health Chatbot" Iconic Research And Engineering Journals Volume 6 Issue 12 2023 Page 302-306
IEEE:
Bhavika Jain , Dr. Sunil Maggu
"Health Chatbot" Iconic Research And Engineering Journals, 6(12)
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