Predictive Modelling for Early Disease Detection from Clinical Text Data using Machine Learning Algorithms
DOI:
https://doi.org/10.48047/CU/54/2/1788-1799Keywords:
Clinical data, prediction, ML, efficiencyAbstract
This paper explores the application of ML algorithms in early disease detection using clinical test data. Research examines various ML approaches across multiple disease categories particularly focusing on blood diseases and respiratory conditions and
neurological issues. The investigation demonstrated how ML techniques can enhance diagnostic accuracy, reduce human error and provide cost effective screening solutions in healthcare set up. Special attention is given to implementation of CNN's and other deep learning architectures in the field of medicine. This study explores significant achievements in detecting conditions like acute lymphoblastic leukaemia (ALL) with an accuracy rate exceeding 99% and showing progress in respiratory disease diagnosis through advanced image analysis and processing. This research shows the growth and integration of AI based diagnosis in clinical practice and their potential to address various healthcare challenges in settings where the resources are limited.
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References
Ahsan, M.M.; Luna, S.A.; Siddique, Z. Machine-Learning-Based Disease Diagnosis: A Comprehensive Review. Healthcare 2022, 10,
https://doi.org/10.3390/healthcare10030541
Sallam, N.M.; Saleh, A.I.; Arafat Ali, H.; Abdelsalam, M.M. An Efficient Strategy for Blood Diseases Detection Based on Grey Wolf Optimization as Feature Selection and Machine Learning Techniques. Appl. Sci. 2022, 12, 10760. https://doi.org/10.3390/app122110760
Zhang, G.; Luo, L.; Zhang, L.; Liu, Z. Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence. Diagnostics 2023, 13, 357. https://doi.org/10.3390/diagnostics13030357
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