AI-Driven Healthcare System for Multi-Disease Prediction and Diagnosis
DOI:
https://doi.org/10.63282/3050-922X.ICAILLMBA-125Keywords:
Disease Prediction, Machine Learning, Deep Learning, Multi-Disease Forecasting, Generative Ai, Chatbot, HealthcareAbstract
Healthcare is fundamental to human well- being. With the rise of AI, especially in Machine Learning (ML) and Deep Learning (DL), healthcare systems have gained powerful tools for disease prediction. However, most models focus on a single disease. This study proposes a multi-disease prediction system using a unified interface that diagnoses conditions such as Diabetes, Heart Disease, Kidney Disease, Parkinson’s, Liver Disease, Brain Cancer, and Breast Cancer. The system integrates ML/DL models, a generative AI-powered chatbot, and a web application built with Flask. Experimental evaluation shows high accuracy, making the system effective for early diagnosis and proactive health management.
References
[1] B.Hu,A.Gaurav,C.Choi,A.Almomani,Evaluatio n and comparative analysis of semantic web- based strategies for enchancing educational system development International Journal on semantic Web and Information Systems(IJSWIS) 18 (1)(2022) 1-14
[2] Prabhavathi, K., Patil, S. (2022). “Tremors and Bradykinesia. In: Arjunan, S.P., Kumar, D.K. (eds) Techniques for Assessment of Parkinsonism for Diagnosis and Rehabilitation”. Series in BioEngineering. Springer. 135–149 https://doi.org/10.1007/978-981-16-3056-9_9
[3] X. Yao, W. Bai, Y. Ren, X. Liu, and Z. Hui, “Exploration of glottal characteristics and the vocal folds behavior for the speech under emotion,” Neurocomputing, vol. 410, no. 10, pp. 328–341, 2020
[4] G. Solana-Lavalle, J.-C. Galán-Hernández, and R. Rosas-Romero, Auto matic Parkinson disease detection at early stages as a pre-diagnosis tool by using classi ers and a small set of vocal features, Biocybern. Biomed. Eng., vol. 40, no. 1, pp. 505516, Jan. 2020.
[5] Y.Ren,H.Fei,X.Liang,D.Ji, and M.Cheng, "A hybrid neural network model for predicting kidney disease in hypertension patients based on electronic health records,"BMC Med. Informat.Decis.Mkaing,vol. 19, n0.S2,Apr.2023
[6] Saber A, Keshk A, Abo-Seida O, Sakr M (2022) Tumor detection and classification in breast mammography based on fine-tuned convolutional neural networks. IJCI Int J Comput Inf 9:74–84
[7] Navoneel Chakrabarty, “Brain MRI Images for Brain Tumor Detection Dataset” , Kaggle , April 2019
[8] S. Sharma and K. Guleria, ‘‘A deep learning based model for the detection of pneumoniafromchestX-rayimagesusingVGG- 16andneural networks,’’ Proc. Comput. Sci., vol. 218, pp. 357–366, Jan. 2023
[9] M. Kaya and Y. Çetin-Kaya, ‘‘A novel ensemble learning framework based on a genetic algorithm for the classification of pneumonia,’’ Eng. Appl. Artif. Intell., vol. 133, Jul. 2024, Art.no. 108494, doi: 10.1016/j.engappai.2024.108494
[10] M. Ali, M. Shahroz, U. Akram, M. F. Mushtaq, S. C. Altamiranda, S. A. Obregon, I. De La Torre Díez, and I. Ashraf, ‘‘Pneumonia detection using chest radiographswithnovelEfficientNetV2Lmodel,’’I EEEAccess, vol. 12, pp. 34691–34707, 2024.
[11] Almasoud M,Ward TE.Detection of chronic kidney disease using deep learning algorithms with least number of predictors .Int J Soft Comput Appl 2023;10
[12] G.S. Monisha et al., “A Enhanced Approach for Identification of Tuberculosis for Chest X-Ray Image Using Machine Learning,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 11S, pp. 443-453, 2023. [CrossRef] [Publisher Link]
[13] Kahn RA, Luo Y, Wu F-X (2022) Machine learning based liver disease diagnosis: a systematic review. Neurocomputing 468:492– 509. https://doi.org/10.1016/j.neucom.2021.08.138
[14] S. Chatterjee, S. Biswas, A. Majee, S. Sen, D. Oliva, R. Sarkar, Breast cancer detection from thermal images using a Grunwald-Letnikov- aided Dragonfly algorithm-based deep feature selection method, Comput. Biol. Med. 141 (2022) 105027.
[15] Bahramiabarghouei H, Porter E, Santorelli A, Gosselin B, Popovíc M, Rusch LA (2023) Flexible 16 antenna array for microwave breast cancer detection. IEEE Trans Biomed Eng 62(10):2516–2525. https:// doi. org/ 10. 1109/TBME. 2015. 24349
