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DepML: An Efficient Machine Learning-Based MDD Detection System in IoMT Framework

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Abstract

This paper aims to propose an automated and less complex machine learning-based depression detection system DepML utilizing the IoMT framework in smart hospitals. This paper focuses on the method to identify relevant features and effective classifiers that can classify depressed and healthy individuals with high accuracy and less complexity. The model implementation is done in three steps: (1) Several essential features, including statistical, time, linear, fractal dimension, non-linear, and coherence, are derived from multichannel EEG. (2) Most relevant features are chosen using three feature selection methods, including Principle component analysis (PCA), Relief-based algorithm (RBA), and Neighbourhood component analysis (NBA). Then, the performance of these feature selection methods is compared, and the best method is used in implementing the model. (3) Classification of normal and depressed subjects is done using five different classifiers, including radial-basis function (RBF)-support vector machine (SVM), logistic regression (LR), K-nearest neighbour (KNN), decision tree (DT), and naïve Bayes classification (NB). The paper concludes that by combining a non-linear feature set and an RBF-SVM classifier, the best classification accuracy of 98.90% is achieved. This paper also concludes that the classification time gets reduced to approximately half after reducing the feature matrix. The results given in this work are utilized to design a depression detection system in smart healthcare and remote applications using IoMT framework.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, India

    Geetanjali Sharma & Amit M. Joshi

  2. Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India

    Emmanuel S. Pilli

  3. Department of Electronics and Communication Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India

    Geetanjali Sharma

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  1. Geetanjali Sharma
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  2. Amit M. Joshi
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  3. Emmanuel S. Pilli
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Correspondence to Geetanjali Sharma.

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“This article is part of the topical collection “Smart and Connected Electronic Systems” guest edited by Amlan Ganguly, Selcuk Kose, Amit M. Joshi and Vineet Sahula”.

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Sharma, G., Joshi, A.M. & Pilli, E.S. DepML: An Efficient Machine Learning-Based MDD Detection System in IoMT Framework. SN COMPUT. SCI. 3, 394 (2022). https://doi.org/10.1007/s42979-022-01250-6

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