Methods for algorithmic diagnosis of metabolic syndrome
| Autor: | Dragan Ivetic, Aleksandar Kupusinac, Darko Ivanović, Milan Vrbaški, Dunja Vrbaski, Edita Stokic, Ksenija Doroslovački |
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| Rok vydání: | 2019 |
| Předmět: |
Male
medicine.medical_specialty Decision tree Medicine (miscellaneous) Type 2 diabetes 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Risk Factors Internal medicine Linear regression medicine Humans 030304 developmental biology Metabolic Syndrome 0303 health sciences Artificial neural network business.industry Middle Aged medicine.disease Random forest Female Neural Networks Computer Metabolic syndrome business Body mass index 030217 neurology & neurosurgery Algorithms Kidney disease |
| Zdroj: | Artificial intelligence in medicine. 101 |
| ISSN: | 1873-2860 |
| Popis: | Metabolic Syndrome (MetS) is associated with the risk of developing chronic disease (atherosclerotic cardiovascular disease, type 2 diabetes, cancers and chronic kidney disease) and has an important role in early prevention. Previous research showed that an artificial neural network (ANN) is a suitable tool for algorithmic MetS diagnostics, that includes solely non-invasive, low-cost and easily-obtainabled (NI&LC&EO) diagnostic methods. This paper considers using four well-known machine learning methods (linear regression, artificial neural network, decision tree and random forest) for MetS predictions and provides their comparison, in order to induce and facilitate development of appropriate medical software by using these methods. Training, validation and testing are conducted on the large dataset that includes 3000 persons. Input vectors are very simple and contain the following parameters: gender, age, body mass index, waist-to-height ratio, systolic and diastolic blood pressures, while the output is MetS diagnosis in true / false form, made in accordance with International Diabetes Federation (IDF). Comparison leads to the conclusion that random forest achieves the highest specificity ( SPC = 0.9436 ), sensitivity ( SNS = 0.9154 ), positive ( PPV = 0.9379 ) and negative ( NPV = 0.9150 ) predictive values. Algorithmic diagnosis of MetS could be beneficial in everyday clinical practice since it can easily identify high risk patients. |
| Databáze: | OpenAIRE |
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