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Analysis Of Machine Learning Methods For Medical Diagnostics

Aryan Gupta | Delhi Public School, RK Puram, India |


Starting out in the 1950s, AI has been contributing to various fields. This paper is a comprehensive review of the profound transformation that AI has brought about in the field of medical diagnostics. The shortcomings in the Drug discovery process and the impact of AI and Machine Learning methods on reducing the time complexity of the process is also briefly discussed. This review includes historical aspects, present utilizations and implications for the future. More specifically, the utilization of algorithms like Decision Trees, Artificial Neural Networks (ANNs), K-Nearest Neighbours (k-NN), Support Vector Machines (SVMs) and Random Forest Classifiers (RFCs) has been discussed in detail, discussing case studies of various medical research papers. The implications of major regulations applied to any consumer medical products and the validity of the AI methods has also been discussed in this paper. To cater to limitations of data, Imaging diagnostics using Convolutional Neural Networks have also been discussed. Moreover, the challenges to medical data collection for diagnoses and the ethical and moral implications of data collection are talked about. The paper also talks about the future scope and the vast potential that AI has in the field of Medical and Imaging Diagnostics. Machine Learning can make proper diagnoses accessible to places where specialized medical professionals may not be available which would significantly reduce the damage caused by misdiagnosis yearly. The studies reviewed show high accuracy and promising results, at times performing to near perfection, resulting in analyses nearly incomparable to human expertise.

Analysis Of Machine Learning Methods For Medical Diagnostics
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