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Pregled bibliografske jedinice broj: 1084487

Comparison of Machine Learning Methods in Classification of Affective Disorders


Kinder, Ivana; Friganović, Krešimir; Vukojević, Jakša; Mulc, Damir; Slukan, Tomislav; Vidović, Domagoj; Brečić, Petrana; Cifrek, Mario
Comparison of Machine Learning Methods in Classification of Affective Disorders // Proceedings 43rd International Convention MIPRO 2020 / Skala, Karolj (ur.).
Rijeka: Croatian Society for Information, Communication and Electronic Technology – MIPRO, 2020. str. 193-197 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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Naslov
Comparison of Machine Learning Methods in Classification of Affective Disorders

Autori
Kinder, Ivana ; Friganović, Krešimir ; Vukojević, Jakša ; Mulc, Damir ; Slukan, Tomislav ; Vidović, Domagoj ; Brečić, Petrana ; Cifrek, Mario

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings 43rd International Convention MIPRO 2020 / Skala, Karolj - Rijeka : Croatian Society for Information, Communication and Electronic Technology – MIPRO, 2020, 193-197

Skup
43rd International Convention MIPRO 2020

Mjesto i datum
Opatija, Hrvatska, 28.09.-02.10.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
electroencephalography ; affective disorders ; depression ; feature selection ; binary classification

Sažetak
Depression belongs to a group of psychiatric disorders called affective disorders. In medical practice, patients are diagnosed according to the criteria in standardized diagnostic manuals. The criteria for diagnosing such disorders focus on the symptoms presented by the patient as well as on disqualifying other potential causes of the symptoms. Electroencephalography (EEG) is a non-invasive brain imaging technique that measures the electrical activity of the brain across different sites on the surface of the scalp. In this paper, 15 EEGs of depression patients and 15 EEGs of healthy control subjects are observed. The depressed and healthy subjects are paired according to age and gender to achieve a dataset that is balanced across classes, gender, and age of subjects. 475 different features are extracted from each EEG and used in the evaluation of different binary classification methods. The best F1-score of 0.7586 is achieved with the K-nearest neighbor algorithm. Sequential feature selection is performed, and sequentially selected features are used to evaluate the former binary classification methods. The best F1-score of 0.8750 is achieved with the K-nearest neighbor algorithm. Classification results are compared across different methods, as well as before and after excluding features that were not deemed significant by the sequential selection algorithm.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Kliničke medicinske znanosti



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Klinika za psihijatriju Vrapče


Citiraj ovu publikaciju

Kinder, Ivana; Friganović, Krešimir; Vukojević, Jakša; Mulc, Damir; Slukan, Tomislav; Vidović, Domagoj; Brečić, Petrana; Cifrek, Mario
Comparison of Machine Learning Methods in Classification of Affective Disorders // Proceedings 43rd International Convention MIPRO 2020 / Skala, Karolj (ur.).
Rijeka: Croatian Society for Information, Communication and Electronic Technology – MIPRO, 2020. str. 193-197 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Kinder, I., Friganović, K., Vukojević, J., Mulc, D., Slukan, T., Vidović, D., Brečić, P. & Cifrek, M. (2020) Comparison of Machine Learning Methods in Classification of Affective Disorders. U: Skala, K. (ur.)Proceedings 43rd International Convention MIPRO 2020.
@article{article, editor = {Skala, K.}, year = {2020}, pages = {193-197}, keywords = {electroencephalography, affective disorders, depression, feature selection, binary classification}, title = {Comparison of Machine Learning Methods in Classification of Affective Disorders}, keyword = {electroencephalography, affective disorders, depression, feature selection, binary classification}, publisher = {Croatian Society for Information, Communication and Electronic Technology – MIPRO}, publisherplace = {Opatija, Hrvatska} }
@article{article, editor = {Skala, K.}, year = {2020}, pages = {193-197}, keywords = {electroencephalography, affective disorders, depression, feature selection, binary classification}, title = {Comparison of Machine Learning Methods in Classification of Affective Disorders}, keyword = {electroencephalography, affective disorders, depression, feature selection, binary classification}, publisher = {Croatian Society for Information, Communication and Electronic Technology – MIPRO}, publisherplace = {Opatija, Hrvatska} }




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