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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Alimi, Sheriff
Babcock University, Ogun State, Nigeria
Computer Science & IT Electrical & Electronics Engineering

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Detection and Estimation of Schizophrenia Severity from Acoustic Features with Inclusion of K-means as Voice Activity Detection Function Alimi, Sheriff; Kuyoro, Afolashade Oluwakemi; Eze, Monday Okpoto; Akande, Oyebola
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.5506

Abstract

Schizophrenia symptom severity estimation provides quantitative information that is useful at both the detection and treatment stages of the mental disorder, as the information helps in decision-making and improves the management of the illness. Very limited studies have been recorded for estimating the symptom severity as a regression task with machine learning, especially from speech recordings, which is the aim of this study coupled with detection. Acoustic features, which comprise frequency-domain and time-domain features, were extracted from 60 schizophrenia subjects and 59 healthy controls enrolled in this research. The acoustic features were used to train GridSearchCV-optimized XGBoost as a classifier. Three Multi-Layer Perceptron (MLP) networks, hyper-parameter-tuned by Bayesian Optimizer, were trained to predict the sub-type symptom severity from acoustic extracted features from the schizophrenia groups. The XGBoost classification model that discriminates between schizophrenia and healthy groups achieved a classification accuracy of 98.6%. The three MLP regression models yielded Mean Absolute Errors of 1.975, 2.856, and 1.555, as well as correlation coefficients of 0.888, 0.806, and 0.786 for predicting positive, negative, and cognitive symptom scores, respectively. Solution architecture for the deployment of the models for practical use was suggested
Co-Authors Akande, Oyebola Eze, Monday Okpoto Kuyoro, Afolashade Oluwakemi