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Stream/Area:
-
Subject:
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Paper ID:
10.55041/IJSREM45860
Volume:
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Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
The current healthcare ecosystem in many regions suffers from inefficiencies such as limited access, lack of early diagnosis, long waiting times, and fragmented services. Doclab is a digital healthcare platform designed to bridge these gaps by offering a comprehensive and user-friendly interface that integrates symptom-based disease prediction, personalized drug recommendations, and medicine delivery. Leveraging both classical machine learning (TF-IDF with Naive Bayes, Logistic Regression, SVM) and advanced deep learning techniques (BiLSTM with Attention), Doclab intelligently processes user input to assist patients in their healthcare journey. This paper details the methodologies, implementation strategies, algorithms used, and potential impact of Doclab, especially in underserved and rural regions, while also addressing data security and system scalability
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Publication Date
4/26/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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