2022
DOI: 10.3390/diagnostics12102526
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Theory and Practice of Integrating Machine Learning and Conventional Statistics in Medical Data Analysis

Abstract: The practice of medical decision making is changing rapidly with the development of innovative computing technologies. The growing interest of data analysis with improvements in big data computer processing methods raises the question of whether machine learning can be integrated with conventional statistics in health research. To help address this knowledge gap, this paper presents a review on the conceptual integration between conventional statistics and machine learning, focusing on the health research. The… Show more

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Cited by 7 publications
(2 citation statements)
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“…"Testing," on the other hand, involves evaluating the performance of the trained models on a separate subset of data that was not used during training. This allows us to assess how well the models generalize to new, unseen data [ 27 , 28 ].…”
Section: Methodsmentioning
confidence: 99%
“…"Testing," on the other hand, involves evaluating the performance of the trained models on a separate subset of data that was not used during training. This allows us to assess how well the models generalize to new, unseen data [ 27 , 28 ].…”
Section: Methodsmentioning
confidence: 99%
“…Dhillon et al examined in one of their works the conceptual integration of traditional statistics and AI, with a primary emphasis on the research undertaken in the field of health. They found that researchers in the field of medicine might look at using AI to supplement traditional statistical methods of decision making in order to obtain additional trustworthy validation metrics [13]. In the healthcare industry, analyzing the trustworthiness of performance helps doctors and other professionals reach their goals by pointing out actions that can help guide decision making, improve performance, and get them started on the path to security and excellence.…”
Section: Introductionmentioning
confidence: 99%