Symptom-Based Classification of Common Syndromes Using Machine Learning: A Review

Authors

  • Soniya Pasi Research Scholar, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India Author
  • Dr. Sheshang Degadwala Professor & Head, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India Author
  • Malini Joshi Assistant Professor, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India Author

DOI:

https://doi.org/10.32628/CSEIT26121

Keywords:

: Symptom Classification, Machine Learning, Disease Prediction, Natural Language Processing, Healthcare AI

Abstract

Symptom-based disease classification has emerged as a critical application of machine learning (ML) and artificial intelligence (AI) in modern healthcare. With the growing availability of electronic health records, patient-reported outcomes, and conversational health systems, automated interpretation of symptoms offers an efficient approach for early diagnosis and clinical decision support. This review systematically analyzes recent advancements in symptom-based classification of common syndromes using traditional machine learning, deep learning, natural language processing (NLP), and large language models (LLMs). The study highlights methods that process structured symptom vectors as well as unstructured free-text symptom descriptions obtained from chatbots and clinical narratives. A comprehensive comparison of twenty recent studies published between 2024 and 2025 is presented, focusing on employed methodologies, key advantages, and reported limitations. Furthermore, the review synthesizes major research findings, identifies open challenges such as data sparsity, explainability, and clinical reliability, and discusses future research directions. This paper aims to serve as a consolidated reference for researchers and practitioners working on intelligent symptom-based disease prediction systems and to guide future development of robust, interpretable, and clinically deployable models.

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Published

05-01-2026

Issue

Vol. 12 No. 1 (2026): January-February

Section

Research Articles

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Copyright (c) 2026 International Journal of Scientific Research in Computer Science, Engineering and Information Technology

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Soniya Pasi, Dr. Sheshang Degadwala, and Malini Joshi, “Symptom-Based Classification of Common Syndromes Using Machine Learning: A Review”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 12, no. 1, pp. 01–06, Jan. 2026, doi: 10.32628/CSEIT26121.