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Title: :  Smart E-Healthcare System
PaperId: :  28233
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 12 Issue 2 2026
DUI:    16.0415/IJARIIE-28233
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Vattikoti SaiSphoorthy Engineering College
Mrs.Fathima ZaheraSphoorthy Engineering College
ManikantaSphoorthy Engineering College
Bhuvana sriSphoorthy Engineering College
K.L.ShivaSphoorthy Engineering College
RakeshSphoorthy Engineering College

Abstract

Computer Engineering
E-healthcare, multi-disease prediction, multi- label classification, clinical decision support, fuzzy matching, symptom analysis, explainable AI, Hamming Loss, F1-score
Healthcare systems are increasingly adopting digital solutions to assist medical professionals in early disease diagnosis and patient monitoring. Existing symptom-based diagnostic systems predominantly focus on single-disease prediction, which fails to reflect real-world clinical scenarios where patients frequently present with overlapping symptoms and comorbid conditions. This paper proposes a Smart E-Healthcare System that leverages multi-label machine learning techniques to predict multiple diseases simultaneously from patient-reported symptoms. The system employs a fuzzy symptom-matching algorithm based on Levenshtein distance similarity to handle input variations and typographical errors, combined with a SHAP-inspired explain- ability module that provides per-symptom contribution scores to enhance clinical transparency and trust. The prediction engine supports eight major diseases—Diabetes, Hypertension, Asthma, Thyroid Disorder, Anemia, Migraine, Arthritis, and Common Cold—each characterized by ten clinically relevant symptoms with associated importance weights. Model performance is eval- uated using appropriate multi-label metrics including Hamming Loss, Precision, Recall, and F1-score. The backend is imple- mented using Node.js with Express.js and a MySQL relational database, while the frontend delivers a responsive, mobile- friendly interface with integrated medicine recommendations and location-based doctor discovery. Experimental evaluation demon- strates a symptom recognition accuracy of 91%, Hamming Loss of 0.08, and macro-averaged F1-score of 0.87, with prediction response times under 50 milliseconds. The system addresses key challenges in e-health including security, scalability, and usability, making it a viable tool for preliminary multi-disease diagnostic support in real-world clinical environments.

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IJARIIE Vattikoti Sai, Mrs.Fathima Zahera, Manikanta, Bhuvana sri, K.L.Shiva, and Rakesh. "Smart E-Healthcare System" International Journal Of Advance Research And Innovative Ideas In Education Volume 12 Issue 2 2026 Page 869-876
MLA Vattikoti Sai, Mrs.Fathima Zahera, Manikanta, Bhuvana sri, K.L.Shiva, and Rakesh. "Smart E-Healthcare System." International Journal Of Advance Research And Innovative Ideas In Education 12.2(2026) : 869-876.
APA Vattikoti Sai, Mrs.Fathima Zahera, Manikanta, Bhuvana sri, K.L.Shiva, & Rakesh. (2026). Smart E-Healthcare System. International Journal Of Advance Research And Innovative Ideas In Education, 12(2), 869-876.
Chicago Vattikoti Sai, Mrs.Fathima Zahera, Manikanta, Bhuvana sri, K.L.Shiva, and Rakesh. "Smart E-Healthcare System." International Journal Of Advance Research And Innovative Ideas In Education 12, no. 2 (2026) : 869-876.
Oxford Vattikoti Sai, Mrs.Fathima Zahera, Manikanta, Bhuvana sri, K.L.Shiva, and Rakesh. 'Smart E-Healthcare System', International Journal Of Advance Research And Innovative Ideas In Education, vol. 12, no. 2, 2026, p. 869-876. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Smart_E_Healthcare_System_ijariie28233.pdf (Accessed : ).
Harvard Vattikoti Sai, Mrs.Fathima Zahera, Manikanta, Bhuvana sri, K.L.Shiva, and Rakesh. (2026) 'Smart E-Healthcare System', International Journal Of Advance Research And Innovative Ideas In Education, 12(2), pp. 869-876IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Smart_E_Healthcare_System_ijariie28233.pdf (Accessed : )
IEEE Vattikoti Sai, Mrs.Fathima Zahera, Manikanta, Bhuvana sri, K.L.Shiva, and Rakesh, "Smart E-Healthcare System," International Journal Of Advance Research And Innovative Ideas In Education, vol. 12, no. 2, pp. 869-876, Mar-App 2026. [Online]. Available: http://ijariie.com/AdminUploadPdf/Smart_E_Healthcare_System_ijariie28233.pdf [Accessed : ].
Turabian Vattikoti Sai, Mrs.Fathima Zahera, Manikanta, Bhuvana sri, K.L.Shiva, and Rakesh. "Smart E-Healthcare System." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 12 number 2 ().
Vancouver Vattikoti Sai, Mrs.Fathima Zahera, Manikanta, Bhuvana sri, K.L.Shiva, and Rakesh. Smart E-Healthcare System. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2026 [Cited : ]; 12(2) : 869-876. Available from: http://ijariie.com/AdminUploadPdf/Smart_E_Healthcare_System_ijariie28233.pdf
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