TY - GEN
T1 - Classification of Psychosomatic's Symptoms of Depression
T2 - 25th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020
AU - Iliou, Theodoros
AU - Konstantopoulou, Georgia
AU - Anastasopoulos, Konstantinos
AU - Lymperopoulou, Christina
AU - Mantas, Georgios
AU - Rodriguez, Jonathan
AU - Lymberopoulos, Dimitrios
AU - Anastassopoulos, George
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9/30
Y1 - 2020/9/30
N2 - In this paper, we propose a novel data preprocessing method in order to facilitate the prediction performance of machine learning algorithms applied on datasets derived from mental patients. In this study, 136 questionnaires were distributed to mental patients - students with psychosomatic problems who were asked to volunteer at the University of Patras Specialty Health Service. The precision of the machine learning methods has to be very high for patients with this kind of issues, in order to achieve the sooner the possible the appropriate treatment. In our research, we used ILIOU data preprocessing method in order to enhance classification techniques for psychosomatic symptoms (i.e., depression). Firstly, we transformed the initial dataset with Principal Component Analysis and ILIOU data preprocessing methods, respectively. Afterwards, for the classification purpose we used seven machine learning classification algorithms with 10-fold cross validation method. According to the classification results, ILIOU preprocessing method led to a classification accuracy of 100% which is suitable for classification and prediction of psychosomatic symptoms.
AB - In this paper, we propose a novel data preprocessing method in order to facilitate the prediction performance of machine learning algorithms applied on datasets derived from mental patients. In this study, 136 questionnaires were distributed to mental patients - students with psychosomatic problems who were asked to volunteer at the University of Patras Specialty Health Service. The precision of the machine learning methods has to be very high for patients with this kind of issues, in order to achieve the sooner the possible the appropriate treatment. In our research, we used ILIOU data preprocessing method in order to enhance classification techniques for psychosomatic symptoms (i.e., depression). Firstly, we transformed the initial dataset with Principal Component Analysis and ILIOU data preprocessing methods, respectively. Afterwards, for the classification purpose we used seven machine learning classification algorithms with 10-fold cross validation method. According to the classification results, ILIOU preprocessing method led to a classification accuracy of 100% which is suitable for classification and prediction of psychosomatic symptoms.
KW - classification algorithms
KW - data mining
KW - Data preprocessing
KW - depression
KW - machine learning
KW - psychosomatic health
U2 - 10.1109/CAMAD50429.2020.9209288
DO - 10.1109/CAMAD50429.2020.9209288
M3 - Conference contribution
AN - SCOPUS:85093985843
T3 - 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
BT - 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 September 2020 through 16 September 2020
ER -