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This paper introduces an AI system for medical guidance and disease prediction with a hybrid architecture of conventional machine learning models and a large language model. The system makes predictions of likely diseases from three symptoms entered by the user through three supervised models, namely Naïve Bayes (NB), Decision Tree (DT), and Random Forest (RF). A majority voting ensemble strategy is employed to enhance the strength and accuracy of predictions. To enable further user support beyond prediction, the system employs the DeepSeek large language model (LLM) for providing intelligent, context-aware medical guidance and prescription recommendations. The proposed architecture is expected to provide a trustworthy and accessible tool for preliminary health evaluation and guidance, filling the gap between symptom checking and specialist consultation. Experimental results demonstrate enhanced accuracy with the majority voting strategy, and the incorporation of DeepSeek LLM provides personalized, uniform medical guidance, showcasing the promise of the synergy between ML and LLMs in digital health
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Cite Article:
"AI-Based Disease Prediction and Guidance Using Symptoms and Language Models", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.a99-a105, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511012.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator