An Improved Chatbot for Predicting Disease and Medicines Using Natural Language Processing with Fuzzy Logic

PhaniRaghava, B.; Kumar, S. Ashok

doi:10.3233/apc220035

Cited by 2 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Above works made us realize that using NLP even though more difficult than DialogFlow and TensorFlow (They need us to define intents) gives us more independence and flexibility in the design of the Chatbot. PhaniRaghavaa and Kumarb (35) aimed to create an artificial conversation entity for healthcare treatment using python. Two algorithms, i.e., fuzzy support vector machine algorithms are compared with the Decision tree algorithm.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Chatbot that Predicts an Illness Dynamically using Machine Learning Techniques

Shedthi B.,

Shetty,

Chadaga

et al. 2024

IJE

View full text Add to dashboard Cite

Timely access to healthcare is crucial in order to maintain a high standard of living. However, obtaining medical consultations can be difficult, especially for those living in remote areas or during a pandemic when face-to-face consultations are not always possible. The ability to accurately diagnose diseases is essential for effective treatment, and recent technological advancements offer a potential solution. Machine learning (ML) and Natural language processing (NLP) enables computer programs to understand human language and extract desired features from responses, allowing for human-like interaction with users. By leveraging these technologies, healthcare professionals can potentially provide more accessible and efficient medical consultations to individuals, regardless of their location. The concept is to establish an online platform where users can ask medical-related queries and receive responses from both medical professionals and fellow users. The platform would feature a Medical Chatbot, which employs advanced ML techniques to analyze user-provided symptoms and provide initial disease diagnosis and related information prior to consulting with a doctor. This disease prediction chatbot interacts dynamically with the users to enter the symptoms of the diseases and based on syntactic and semantic similarity response is given. In this work the threshold of similarity score is kept of 0.7. K-Nearest neighbors, Random forest, Support vector machine, Naive bayes and Logistic regression algorithms are used for prediction of disease based on symptoms which are faced by users. The syntactic similarity, fuzzy string matching and semantic similarity using all-MiniLM-L6-v2 model is used to improve the efficiency of the result.

show abstract

Section: Related Workmentioning

confidence: 99%