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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM60732
Volume:
10 |
4 |
April 2026
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.
â DiagnoSure is an Artificial Intelligence (AI)- powered healthcare support system designed to assist in preliminary disease prediction using patient-provided symptoms. The system addresses the challenge of processing unstructured medical inputs expressed in natural language. A domain-specific Natural Language Processing (NLP) model, BioBERT (Bidirectional Encoder Representations from Transformers for biomedical text), is utilized to extract relevant medical features from free-text symptom descriptions [1]. These features are processed using Extreme Gradient Boosting (XGBoost) to generate disease predictions along with associated probability scores [2]. To ensure transparency and interpretability, SHapley Additive exPlanations (SHAP) is integrated to identify the contribution of individual symptoms to prediction outcomes [7]. The system also supports structured inputs, enabling efficient processing in Electronic Health Record (EHR) scenarios [9]. The proposed approach demonstrates reliable and interpretable predictions and serves as a decision-support tool for early health assessment.
KEYWORDS
Publication Date
4/20/2026
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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