Paper Title
Medical Virtual Assistant - Seamless Care, Virtual and Everywhere
Article Identifiers
Authors
Tejaswini Rasam , Isha Kalbhor , Darshan Taskar , Harshad Khalate , Prof. Vandana Dixit
Keywords
Disease Prediction, Gaussian Naïve Bayes Algorithm, Medicine Recommendation
Abstract
The "Medical Virtual Assistant" project aims to revolutionize healthcare accessibility by harnessing the power of artificial intelligence and machine learning in a user-friendly Android application. This innovative application empowers users to input their symptoms via voice or text and receive accurate disease predictions, medication recommendations, and access to professional medical advice. Leveraging a comprehensive dataset and integrating machine learning models powered by the Gaussian Naive Bayes algorithm. The "Medical Virtual Assistant" provides personalized healthcare guidance, thus bridging the gap between patients and medical expertise. The project encompasses two user-oriented modules: one for patients and another for healthcare professionals. Patients can input symptoms and receive instant disease predictions, medication suggestions, and referrals to healthcare providers, all while enjoying the flexibility of voice or text input. Additionally, patients have the option to request medication verification from doctors, ensuring their safety and well-being. The doctor module allows healthcare professionals to review patients' requests, provide medication verification, and extend their expertise to supplement the recommendations. This project promotes a collaborative approach to healthcare, where patients and doctors work in unison to enhance medical outcomes and ensure accurate and safe medical advice. This multifaceted application comprises three robust models aimed at addressing critical healthcare needs. The first model focuses on accurately predicting diseases based on user-provided symptoms, leveraging machine learning algorithms trained on comprehensive medical datasets. The second model utilizes disease, gender, and age inputs to recommend appropriate medications, providing tailored suggestions for effective treatments. Additionally, the third model suggests suitable healthcare professionals based on predicted diseases, ensuring users can swiftly access expert medical guidance. With a commitment to quality, accuracy, and user-centric design, the "Medical Virtual Assistant" project aims to revolutionize the way individuals seek healthcare advice, empowering them with knowledge and enabling healthcare professionals to make a positive impact on patients' well-being. This innovative solution has the potential to shape the future of healthcare accessibility, ensuring that users receive accurate, timely, and personalized healthcare guidance at their fingertips.
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How To Cite (APA)
Tejaswini Rasam, Isha Kalbhor, Darshan Taskar, Harshad Khalate, & Prof. Vandana Dixit (March-2024). Medical Virtual Assistant - Seamless Care, Virtual and Everywhere. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), c713-c718. https://ijnrd.org/papers/IJNRD2403288.pdf
Issue
Volume 9 Issue 3, March-2024
Pages : c713-c718
Other Publication Details
Paper Reg. ID: IJNRD_215714
Published Paper Id: IJNRD2403288
Downloads: 000121978
Research Area: Information TechnologyÂ
Country: Pune, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2403288.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403288
About Publisher
Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
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Licence
This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.
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