The present work aims at the evaluation of the effectiveness of different machine learning algorithms on a variety of clinical data, derived from small, medium, and large publicly available databases. To this end, several algorithms were tested, and their performance, both in terms of accuracy and time required for the training and testing phases, are here reported. Sometimes a data preprocessing phase was also deemed necessary to improve the performance of the machine learning procedures, in order to reduce the problem size. In such cases a detailed analysis of the compression strategy and results is also presented.
A comparative study of machine learning algorithms for physiological signal classification / Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Tanoni, Giulia; Turchetti, Claudio. - In: PROCEDIA COMPUTER SCIENCE. - ISSN 1877-0509. - ELETTRONICO. - 126:(2018), pp. 1977-1984. (Intervento presentato al convegno 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2018) tenutosi a Belgrade, Serbia nel 3 - 5 September 2018) [10.1016/j.procs.2018.07.255].
A comparative study of machine learning algorithms for physiological signal classification
Giorgio Biagetti;Paolo Crippa
;Laura Falaschetti;Claudio Turchetti
2018-01-01
Abstract
The present work aims at the evaluation of the effectiveness of different machine learning algorithms on a variety of clinical data, derived from small, medium, and large publicly available databases. To this end, several algorithms were tested, and their performance, both in terms of accuracy and time required for the training and testing phases, are here reported. Sometimes a data preprocessing phase was also deemed necessary to improve the performance of the machine learning procedures, in order to reduce the problem size. In such cases a detailed analysis of the compression strategy and results is also presented.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
