International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Smart Healthcare System Using Machine Learning for Predictive Diagnosis and Treatment Planning

B. RupadeviAssociate Professor, Dept. of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, AP, IndiaMadduru GopikrishnaPost Graduate, Dept. of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, AP, India

Vol 9 No 2025 (2025): Volume 9, Special Issue of ICCIS-2025 May 2025 | Pages: 92-97

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 11-06-2025

doi Logo doi.org/10.47001/IRJIET/2025.ICCIS-202514

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Abstract

Early disease identification is crucial for prompt treatment and improved patient outcomes in the modern healthcare system. This study presents a machine learning-based disease prediction and medical recommendation system that analyses symptoms and offers precise health insights. A well- structured dataset comprising disease categories, suggested therapies, and symptom severity forms the foundation of the system. It provides individualized nutritional and medical advice by using a classification-based predictive model to analyse symptom patterns and propose potential diagnoses. We have created a web-based interface to make the system easier to use, enabling users to enter their symptoms and get immediate medical advice. This method allows users to conduct a preliminary self- evaluation prior to requesting expert assistance, thereby bridging the gap between patients and medical advice. This technology provides rapid and accurate health insights, making it an invaluable resource for well-informed healthcare decision- making.

Keywords

Disease Prediction System, Machine Learning in Healthcare, Random Forest Classifier, Flask Web Application, Symptom-Based Diagnosis, Medical Recommendations


Citation of this Article

B. Rupadevi, & Madduru Gopikrishna. (2025). Smart Healthcare System Using Machine Learning for Predictive Diagnosis and Treatment Planning. In proceeding of Second International Conference on Computing and Intelligent Systems (ICCIS-2025), published in IRJIET, Volume 9, Special Issue ICCIS-2025, pp 92-97. Article DOI https://doi.org/10.47001/IRJIET/2025.ICCIS-202514

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References
  1. Courtiol, P., et al. (2019). Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nature Medicine, 25(10), 1519– 1525.
  2. Schmauch, B., et al. (2020). A deep learning model to predict RNA-Seq expression of tumors from whole slide images. Nature Communications, 11(1), 3877.
  3. Bron, E. E., Smits, M., Niessen, W. J., & Klein, S. (2015). Feature selection based on the SVM weight vector for classification of dementia. IEEE Journal of Biomedical and Health Informatics, 19(5), 1617–1626.
  4. Gorji, H. T., & Haddadnia, J. (2015). A novel method for early diagnosis of Alzheimer's disease based on pseudo Zernike moment from structural MRI. Neuroscience, 305, 361–371.
  5. Suk, H.-I., & Shen, D. (2014). Clustering- induced multi-task learning for AD/MCI classification. Medical Image Computing and Computer-Assisted Intervention, 17(Pt 2), 393–401.
  6. Liu, M., Zhang, D., Adeli, E., & Shen, D. (2016). Inherent structure-based multiview learning with multitemplate feature representation for Alzheimer's disease diagnosis. IEEE Transactions on Biomedical Engineering, 63(7), 1478–1486.
  7. Zu, C., Jie, B., Liu, M., Chen, S., & Shen, D. (2016). Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment. Brain Imaging and Behavior, 10(4), 1148–1159.
  8. Zhu, X., Suk, H.-I., & Shen, D. (2014). A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. NeuroImage, 100, 91–105.
  9. Suk, H.-I., Lee, S.-W., & Shen, D. (2016). Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis. Brain Structure and Function, 221(5), 2569–2587.

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