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Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment

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Neonatal Intensive Care Unit, Department of Surgical Sciences, AOU and University of Cagliari, 09042 Cagliari, Italy
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Marie Sklodowska-Curie CAPICE Project, Department of Surgical Sciences, University of Cagliari, 09042 Cagliari, Italy
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Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, 37129 Verona, Italy
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Section of Psychiatry, Department of Medical Science and Public Health, University of Cagliari, 09125 Cagliari, Italy
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Department of Pharmacology, Dalhousie University, Halifax 6299, NS B3H 4R2, Canada
*
Author to whom correspondence should be addressed.
Medicina 2020, 56(9), 455; https://doi.org/10.3390/medicina56090455
Received: 15 July 2020 / Revised: 2 September 2020 / Accepted: 7 September 2020 / Published: 8 September 2020
(This article belongs to the Section Psychiatry)
Futurists have anticipated that novel autonomous technologies, embedded with machine learning (ML), will substantially influence healthcare. ML is focused on making predictions as accurate as possible, while traditional statistical models are aimed at inferring relationships between variables. The benefits of ML comprise flexibility and scalability compared with conventional statistical approaches, which makes it deployable for several tasks, such as diagnosis and classification, and survival predictions. However, much of ML-based analysis remains scattered, lacking a cohesive structure. There is a need to evaluate and compare the performance of well-developed conventional statistical methods and ML on patient outcomes, such as survival, response to treatment, and patient-reported outcomes (PROs). In this article, we compare the usefulness and limitations of traditional statistical methods and ML, when applied to the medical field. Traditional statistical methods seem to be more useful when the number of cases largely exceeds the number of variables under study and a priori knowledge on the topic under study is substantial such as in public health. ML could be more suited in highly innovative fields with a huge bulk of data, such as omics, radiodiagnostics, drug development, and personalized treatment. Integration of the two approaches should be preferred over a unidirectional choice of either approach. View Full-Text
Keywords: machine learning; medicine; healthcare; diagnosis; drug development; personalized treatment; autonomous technology machine learning; medicine; healthcare; diagnosis; drug development; personalized treatment; autonomous technology
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Rajula, H.S.R.; Verlato, G.; Manchia, M.; Antonucci, N.; Fanos, V. Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment. Medicina 2020, 56, 455.

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