An Innovative Frontier in Healthcare: Optimizing Alcoholism Detection with an Assorted Convolutional Neural Network and Long Short-Term Memory System

International Journal of Electronics and Communication Engineering

© 2026 by SSRG - IJECE Journal

Volume 13 Issue 1

Year of Publication : 2026

Authors : G. Usha, K. Narasimhan

10.14445/23488549/IJECE-V13I1P107

How to Cite?

G. Usha, K. Narasimhan, "An Innovative Frontier in Healthcare: Optimizing Alcoholism Detection with an Assorted Convolutional Neural Network and Long Short-Term Memory System," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 1, pp. 74-88, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I1P107

Abstract:

Alcohol is considered an intense-affective agent in brain functions that can cause abrupt health issues. Hence, the predominant approach for detecting alcohol consumption is to perform an alcohol diagnosis. Although clinical applications for determining these causes have widely evolved, classical processes have several drawbacks for generating favorable facilities. Clinicians have increasingly developed methods for determining consumption on a technological basis in recent years. Basically, EEG tools aid in exhibiting brain activity through EEG signals. Currently, instigated technology is relatively dependent on ML techniques; however, it has major defects, such as poor spatial resolution evaluation and high computational requirements for precise outcomes. Therefore, the proposed model utilizes a DL approach in which both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) are used to analyze spatial data to further define model enhancement. The proposed research utilized ten types of pretrained neural network models for image classification. For comprehending the data samples, the proposed system used the EEG-Alcohol dataset to evaluate model performance and efficiency on classification, and was examined by the accuracy metric. In essence, comparative analysis is conducted through the respective models and their comparison with existing research that applied DL-based techniques, unifying LSTM to CNN custom, which led to prominence in the classification of alcoholic and nonalcoholic EEG signals, where the highest accuracy attained by the proposed model is VGG19+LSTM, with an accuracy rate of 96.72%. Furthermore, the model intends to contribute to therapeutic services while pivoting as an impactful intervention in patient care.

Keywords:

Deep Learning, CNN, LSTM, EEG Signals, Alcoholism.

References:

[1] J. Morris et al., “The "Alcoholic Other": Harmful Drinkers Resist Problem Recognition to Manage Identity Threat,” Addictive Behaviors, vol. 124, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Palak Mahajan et al., “Ensemble Learning for Disease Prediction: A Review,” Healthcare, vol. 11, no. 12, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Daniel Q. Huang, “Global Epidemiology of Alcohol-Associated Cirrhosis and HCC: Trends, Projections and Risk Factors,” Nature Reviews Gastroenterology & Hepatology, vol. 20, pp. 37-49, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Xavier Noël, “Leveraging Memory Suppression from a Goal-Directed Perspective to Regain Control over Alcohol Consumption,” Alcoholism, Clinical and Experimental Research, vol. 48, no. 12, pp. 2242-2245, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Kaloso M. Tlotleng, and Rodrigo S. Jamisola, “An Analysis of the Severity of Alcohol Use Disorder Based on Electroencephalography using Unsupervised Machine Learning,” Big Data and Cognitive Computing, vol. 9, no. 7, pp. 1-27, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Niyozov Shuxrat Toshtemirovich, Turayev Bobir Temirpulotovich, and Mamurova Mavludaxon Mirxamzayevna, “Modern Methods of Diagnosing and Treating Neurological Changes Observed in Alcoholism,” Journal of Medical Genetics and Clinical Biology, vol. 2, no. 1, pp. 107-116, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Maytal Wolfe et al., “Alcohol and the Central Nervous System,” Practical Neurology, vol. 23, no. 4, pp. 273-285, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Muhammad Tariq Sadiq et al., “Alcoholic EEG Signals Recognition based on Phase Space Dynamic and Geometrical Features,” Chaos, Solitons & Fractals, vol. 158, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Subrata Pain et al., “Detection of Alcoholism by Combining EEG Local Activations with Brain Connectivity Features and Graph Neural Network,” Biomedical Signal Processing and Control, vol. 85, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Hamid Mukhtar, Saeed Mian Qaisar, and Atef Zaguia, “Deep Convolutional Neural Network Regularization for Alcoholism Detection using EEG Signals,” Sensors, vol. 21, no. 16, pp. 1-19, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Said Abenna, Mohammed Nahid, and Hamid Bouyghf, “Alcohol use Disorders Automatic Detection based BCI Systems: A Novel EEG Classification based on Machine Learning and Optimization Algorithms,” International Journal of Information Science and Technology, vol. 6, no. 1, pp. 14-25, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Salman Ahmed et al., “An Empirical Analysis of State-of-Art Classification Models in an it Incident Severity Prediction Framework,” Applied Sciences, vol. 13, no. 6, pp. 1-27, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] D. Merlin Praveena, D. Angelin Sarah, and S. Thomas George, “Deep Learning Techniques for EEG Signal Applications–A Review,” IETE Journal of Research, vol. 68, no. 4, pp. 3030-3037, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Muhammad Tariq Sadiq et al., “Efficient Novel Network and Index for Alcoholism Detection from EEGs,” Health Information Science and Systems, vol. 11, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Hamid Mukhtar, Chapter 11 - EEG Signal Processing with Deep Learning for Alcoholism Detection, Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction: Artificial Intelligence Applications in Healthcare and Medicine, pp. 211-226, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Levi N. Bonnell et al., “A Machine Learning Approach to Identification of Unhealthy Drinking,” The Journal of the American Board of Family Medicine, vol. 33, no. 3, pp. 397-406, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] L. Farsi et al., “Classification of Alcoholic EEG Signals Using a Deep Learning Method,” IEEE Sensors Journal, vol. 21, no. 3, pp. 3552-3560, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Qasem Abu Al-Haija, and Moez Krichen, “A Lightweight in-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning,” Computers, vol. 11, no. 8, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Dennis Flathau et al., “Early Detection of Alcohol Use Disorder Based on a Novel Machine Learning Approach using EEG Data,” 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, pp. 3897-3904, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Sandeep Bavkar, Brijesh Iyer, and Shankar Deosarkar, “Rapid Screening of Alcoholism: An EEG based Optimal Channel Selection Approach,” IEEE Access, vol. 7, pp. 99670-99682, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Jardel das C. Rodrigues et al., “Classification of EEG Signals to Detect Alcoholism Using Machine Learning Techniques,” Pattern Recognition Letters, vol. 125, pp. 140-149, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Gaowei Xu et al., “A Deep Transfer Convolutional Neural Network Framework for EEG Signal Classification,” IEEE Access, vol. 7, pp. 112767-112776, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Fatima Faraz et al., Classification of Normal and Alcoholic EEG Signals Using Signal Processing and Machine Learning, Artificial Intelligence Enabled Signal Processing based Models for Neural Information Processing, CRC Press, pp. 33-50, 2024.
[Google Scholar] [Publisher Link]
[24] Liu Jiajie et al., “Clinical Decision Support System for Alcoholism Detection Using the Analysis of EEG Signals,” IEEE Access, vol. 6, pp. 61457-61461, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Mingkan Shen et al., “Detection of Alcoholic EEG Signals Based on Whole Brain Connectivity and Convolution Neural Networks,” Biomedical Signal Processing and Control, vol. 79, 2023,
[CrossRef] [Google Scholar] [Publisher Link]
[26] Hongyi Zhang et al., “Bi-Dimensional Approach based on Transfer Learning for Alcoholism Pre-Disposition Classification via EEG Signals,” Frontiers in Human Neuroscience, vol. 14, pp. 1-13, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Nur Shahirah Md Nor et al., “Automated Classification of Eight Different Electroencephalogram (EEG) Bands using Hybrid of Fast Fourier Transform (FFT) with Machine Learning Methods,” Neuroscience Research Notes, vol. 5, no. 1, pp. 1-12, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Yue Zhao et al., “E3GCAPS: Efficient EEG-based Multi-Capsule Framework with Dynamic Attention for Cross-Subject Cognitive State Detection,” China Communications, vol. 19, no. 2, pp. 73-89, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Tatyana Yakovleva, and A.V. Krysko, “Processing Alcoholism EEG Signals Using Neural Networks,” Russian Journal of Biomechanics, vol. 28, no. 1, pp. 110-126, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Gowri Shankar Manivannan et al., “Detection of Alcoholic EEG signal using LASSO Regression with Metaheuristics Algorithms based LSTM and Enhanced Artificial Neural Network Classification Algorithms,” Scientific Reports, vol. 14, no. 1, pp. 1-24, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Neeraj et al., “Augmenting Common Spatial Patterns to Deep Learning Networks for Improved Alcoholism Detection using EEG Signals,” Computers in Biology and Medicine, vol. 193, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Supreyo Chakravorty Pretom et al., “Alcoholism Detection from Electroencephalogram Using Machine Learning and Deep Learning,” 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), Chittagong, Bangladesh, pp. 1-6, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Savarapu Chandra Shekar et al., “Detection of Alcoholic using LSTM and Enhanced ANN Classification Algorithms,” 2025 6th International Conference for Emerging Technology (INCET), Belgaum, India, pp. 1-6, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Emad-ul-Haq Qazi, Muhammad Hussain, and Hatim A. AboAlsamh, “Electroencephalogram (EEG) Brain Signals to Detect Alcoholism Based on Deep Learning,” Computers, Materials & Continua, vol. 67, no. 3, pp. 3329-3348, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Nandini Kumari, Shamama Anwar, and Vandana Bhattacharjee, “A Deep Learning-Based Approach for Accurate Diagnosis of Alcohol Usage Severity using EEG Signals,” IETE Journal of Research, vol. 69, no. 11, pp. 7816-7830, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Seffi Cohen et al., “Ensemble Learning for Alcoholism Classification using EEG Signals,” IEEE Sensors Journal, vol. 23, no. 15, pp. 17714-17724, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Sagnik De, Anurag Singh, and Ashish Kumar Bhandari, “A Novel Vision Transformer based Multimodal Fusion Approach for Clinical MDD Diagnosis Using EEG and Audio Signals,” IEEE Transactions on Computational Biology and Bioinformatics, vol. 22, no. 6, pp. 3399-3409, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Gourav Siddhad et al., “Efficacy of Transformer Networks for Classification of EEG Data,” Biomedical Signal Processing and Control, vol. 87, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Yuji Bai, and Lin Yu, “Evaluating a Deep Learning Model for EEG Categorization of Alcoholic and Non-Alcoholic Subjects,” Journal of Ambient Intelligence and Humanized Computing, vol. 16, pp. 523-532, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[40] EEG-Alcohol, Kaggle. [Online]. Available: https://www.kaggle.com/datasets/nnair25/Alcoholics/data