Volume : 9, Issue : 6, JUN 2023

NATIONAL CONFERENCE ON INNOVATIONS IN COMPUTING TECHNOLOGIES (NCICT'23)

A REVIEW ON ARTIFICIAL INTELLIGENCE IN CLASSIFICATION OF DISEASES

DR. M. YUVARAJU*, V. NARENTHIRUVALAR, T. L. KAYATHRI

Abstract

Artificial intelligence can assist Medical field through a range of patient care and intelligent health systems. For the diagnosis of diseases, the development of new drugs, and the identification of patient vulnerabilities, artificial intelligence techniques ranging from machine learning to deep learning have become prevalent in healthcare. To precisely determine diseases using artificial intelligence algorithms, numerous medical data sources are required. As artificial intelligence (AI) technology progresses, it is currently being utilized to diagnose a wide range of medical conditions. This AI technology is powered by big data and has significant computation and learning power. This study examines the use of expert systems, neural networks, and deep learning in identifying diseases via AI technology. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, Parkinson, Heart Disease, Chronic Kidney disease, Chronic Lung disease, Eye disease, Covid-19 and so on.

Keywords

ARTIFICIAL INTELLIGENCE, DISEASE, ARTIFICIAL NEURAL NETWORK, DEEP LEARNING.

Article : Download PDF

Cite This Article

-

Article No : 40

Number of Downloads : 705

References

1. R. C. Joshi, J. S. Khan, V. K. Pathak and M. K. Dutta, "AI-CardioCare: Artificial Intelligence Based Device for Cardiac Health Monitoring," in IEEE Transactions on Human-Machine Systems, vol. 52, no. 6, pp. 1292-1302, Dec. 2022, doi: 10.1109/THMS.2022.3211460.

2. M. Tanveer, A. H. Rashid, M. A. Ganaie, M. Reza, I. Razzak and K. -L. Hua, "Classification of Alzheimer’s Disease Using Ensemble of Deep Neural Networks Trained Through Transfer Learning," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 4, pp. 1453-1463, April 2022, doi: 10.1109/JBHI.2021.3083274.

3. M. Kaya Kele? and Ü. Kiliç, "Classification of Brain Volumetric Data to Determine Alzheimer’s Disease Using Artificial Bee Colony Algorithm as Feature Selector," in IEEE Access, vol. 10, pp. 82989-83001, 2022, doi: 10.1109/ACCESS.2022.3196649.

4. O. Bernabé, E. Acevedo, A. Acevedo, R. Carreño and S. Gómez, "Classification of Eye Diseases in Fundus Images," in IEEE Access, vol. 9, pp. 101267-101276, 2021, doi: 10.1109/ACCESS.2021.3094649.

5. S. Akter et al., "Comprehensive Performance Assessment of Deep Learning Models in Early Prediction and Risk Identification of Chronic Kidney Disease," in IEEE Access, vol. 9, pp. 165184-165206, 2021, doi: 10.1109/ACCESS.2021.3129491.

6. M. B. Abubaker and B. Babayi?it, "Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Methods," in IEEE Transactions on Artificial Intelligence, vol. 4, no. 2, pp. 373-382, April 2023, doi: 10.1109/TAI.2022.3159505.

7. K. AlSharabi, Y. Bin Salamah, A. M. Abdurraqeeb, M. Aljalal and F. A. Alturki, "EEG Signal Processing for Alzheimer’s Disorders Using Discrete Wavelet Transform and Machine Learning Approaches," in IEEE Access, vol. 10, pp. 89781-89797, 2022, doi: 10.1109/ACCESS.2022.3198988.

8. J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan and A. Saboor, "Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare," in IEEE Access, vol. 8, pp. 107562-107582, 2020, doi: 10.1109/ACCESS.2020.3001149.

9. R. Alkhatib, M. O. Diab, C. Corbier and M. E. Badaoui, "Machine Learning Algorithm for Gait Analysis and Classification on Early Detection of Parkinson," in IEEE Sensors Letters, vol. 4, no. 6, pp. 1-4, June 2020, Art no. 6000604, doi: 10.1109/LSENS.2020.2994938.

10. Z. Huang et al., "Parkinson’s Disease Classification and Clinical Score Regression via United Embedding and Sparse Learning From Longitudinal Data," in IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 8, pp. 3357-3371, Aug. 2022, doi: 10.1109/TNNLS.2021.3052652.

11. H. Yang, Z. Chen, H. Yang and M. Tian, "Predicting Coronary Heart Disease Using an Improved LightGBM Model: Performance Analysis and Comparison," in IEEE Access, vol. 11, pp. 23366-23380, 2023, doi: 10.1109/ACCESS.2023.3253885.

12. P. Chittora et al., "Prediction of Chronic Kidney Disease - A Machine Learning Perspective," in IEEE Access, vol. 9, pp. 17312-17334, 2021, doi: 10.1109/ACCESS.2021.3053763.

13. K. P. Exarchos et al., "Review of Artificial Intelligence Techniques in Chronic Obstructive Lung Disease," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 5, pp. 2331-2338, May 2022, doi: 10.1109/JBHI.2021.3135838.

14. F. J. Martinez-Murcia, A. Ortiz, J. -M. Gorriz, J. Ramirez and D. Castillo-Barnes, "Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders," in IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 1, pp. 17-26, Jan. 2020, doi: 10.1109/JBHI.2019.2914970.

15. A. Loddo, G. Meloni and B. Pes, "Using Artificial Intelligence for COVID-19 Detection in Blood Exams: A Comparative Analysis," in IEEE Access, vol. 10, pp. 119593-119606, 2022, doi: 10.1109/ACCESS.2022.3221750.