Abstract: Large and complex datasets in the quickly changing field of healthcare defy conventional research methods due to their sheer number, minute details, and fast-paced nature. Techniques that can efficiently handle and analyze large datasets—including clinical, personal computer, and medical usage data—are desperately needed. Conventional statistical models face difficulties because, in spite of their vastness, these datasets are either incomplete or restricted to particular segments of the population. Although machine learning approaches have demonstrated their ability to overcome these obstacles, they are not impervious to the biases that are frequently present in observational studies. For these models to be reliable and accurate in research applications, they must be rigorously validated using industry-standard testing techniques like lasso or ridge regression.
Keywords: Machine Learning (ML), Healthcare, Deep Learning, Big Data, Artificial Intelligence (AI), Predictive Analytics, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Data Mining
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DOI:
10.17148/IJARCCE.2022.111016
