Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis
This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequen...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/10/2/21 |
| _version_ | 1797571895951884288 |
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| author | Gopi Battineni Getu Gamo Sagaro Nalini Chinatalapudi Francesco Amenta |
| author_facet | Gopi Battineni Getu Gamo Sagaro Nalini Chinatalapudi Francesco Amenta |
| author_sort | Gopi Battineni |
| collection | DOAJ |
| description | This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future. |
| first_indexed | 2024-03-10T20:47:07Z |
| format | Article |
| id | doaj.art-a8bfd18fc33d4a41ba4f232452dc4665 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-10T20:47:07Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-a8bfd18fc33d4a41ba4f232452dc46652023-11-19T20:13:02ZengMDPI AGJournal of Personalized Medicine2075-44262020-03-011022110.3390/jpm10020021Applications of Machine Learning Predictive Models in the Chronic Disease DiagnosisGopi Battineni0Getu Gamo Sagaro1Nalini Chinatalapudi2Francesco Amenta3Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, ItalyCenter for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, ItalyCenter for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, ItalyCenter for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, ItalyThis paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.https://www.mdpi.com/2075-4426/10/2/21chronic diseasesprediction modelspathologiesaccuracydisease classification |
| spellingShingle | Gopi Battineni Getu Gamo Sagaro Nalini Chinatalapudi Francesco Amenta Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis Journal of Personalized Medicine chronic diseases prediction models pathologies accuracy disease classification |
| title | Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis |
| title_full | Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis |
| title_fullStr | Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis |
| title_full_unstemmed | Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis |
| title_short | Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis |
| title_sort | applications of machine learning predictive models in the chronic disease diagnosis |
| topic | chronic diseases prediction models pathologies accuracy disease classification |
| url | https://www.mdpi.com/2075-4426/10/2/21 |
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