Early prediction of cognitive impairments using physiological signal for enhanced socioeconomic status |
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| Institution: | 1. School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, 212100, China;2. School of Economics and Management, Shangqiu Normal University, Shangqiu, 476000, China;3. School of Management, Guangzhou Xinhua University, Dongguan, 523133, China;4. School of Business, Sun Yat-Sen University, Guangzhou, 310003, China;1. AGH University of Science and Technology, 30 Mickiewicza Ave, Kraków 30-059, Poland;2. VSB Technical University of Ostrava, 17. listopadu 2172/15, Ostrava-Poruba 708 00, Czech Republic;1. School of Economics and Management, Chang''an University, Xi''an 710064, China;2. Computer & Information Sciences Department, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia;3. Institute of IR4.0, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia;4. College of Engineering, Al Ain University, Al Ain, United Arab Emirates;5. Department of Mathematics, College of Science, Tafila Technical University, Tafila, Jordan |
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| Abstract: | Cognitive impairments like memory disorder and depressive disorders lead to fatal consequences if proper attention is not given to such health hazards. Their impact is extended to the socioeconomic status of the developed and low or middle-income countries in terms of loss of talented and skilled population. Additionally, financial burden is borne by the countries in terms of additional health budget allotment. This paper presents a novel strategy for early detection of cognitive deficiency to eliminate the economic repercussions caused by memory disorder and depressive disorders. In this work, Electroencephalogram (EEG) and a word learning neuropsychological test, i.e. California Verbal Learning Task (CVLT), are conjunctively used for memory assessment. The features of EEG and scores of CVLT are modeled by applying different machine learning techniques, namely K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). Comparatively, experimental results have better classification accuracy than the existing schemes that considered EEG for estimating cognitive heuristics. More specifically, SVM attains the highest accuracy score of 81.56% among all machine learning algorithms, which can assist in the early detection of cognitive impairments. The proposed strategy can be helpful in clinical diagnosis of psychological health and improving quality of life as a whole. |
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| Keywords: | Electroencephalography Cognitive load Machine learning Healthcare costs CVLT Working memory |
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