Article contents

Paper title
Author(s)
Abstract
Article information
How to cite?
DOI
Copyright
Open access
References
Research Article

Advancing Neurological Disease Prediction through Machine Learning Techniques

Authors

  • Shariar Islam Saimon Department of Computer Science, School of Engineering, University of Bridgeport, USA
  • Intiser Islam Department of Computer Science, School of Engineering, University of Bridgeport, USA
  • Shake Ibna Abir Instructor of Mathematics, Department of Mathematics and Statistics, Arkansas State University, Arkansas, USA
  • Nigar Sultana Department of Finance, University of New Haven, CT, USA
  • Md Sanjit Hossain MBA in Information Technology Management, University of the Potomac, Washington DC, USA
  • Sarder Abdulla Al Shiam Department of Management–Business Analytics, St Francis College, New York, USA

Abstract

Late prediction is a major health problem for neurological diseases and early prediction is essential to advance patient outcomes and allow timely intervention. Machine learning (ML) advances are enabling doctors to more efficiently and innovatively predict the onset of neurological conditions using complex biomedical data. In this study the assessment of the power of different ML algorithms to predict Parkinson’s disease, epilepsy, and multiple sclerosis is done to evaluate the relative performance and practical applications. In order to determine the effectiveness of ML techniques, a comprehensive review was done on the various ML techniques e.g. decision trees, k-nearest neighbors (KNN) and ensemble methods. Furthermore, the study validates the predictive capabilities of these approaches, using the Gradient Boosting and Support Vector Machines (SVM) for a case study on EEG and for EEG and clinical datasets. The models were evaluated and compared with respect to known key performance metrics such as accuracy, sensitivity and specificity. Results showed that Gradient Boosting performed best, and with an accuracy of 89% it could predict Parkinson’s earlier on in its first stages. In detecting seizure activity, KNN was very successful accounting for an accuracy of 85%, making it a useful tool for epilepsy diagnosis. The study demonstrated robust generalizability across diverse datasets with ensemble methods, broadly applicable to wider populations for neurological disease prediction. Finally, the study demonstrates that machine learning provides a highly flexible and efficient paradigm for making predictions of neurological disease, with potential for early diagnosis and intervention. The results suggest that ML can be a powerful tool to analyze very complex biomedical data, and in turn develop diagnostic tools targeted towards certain neurological disorders. The integration of ML models with real time clinical systems, and the extension of this to other diseases will further improve diagnostic precision and access in clinical practice.

Article information

Journal

Journal of Computer Science and Technology Studies

Volume (Issue)

7 (1)

Pages

139-156

Published

2025-02-07

How to Cite

Shariar Islam Saimon, Intiser Islam, Shake Ibna Abir, Nigar Sultana, Md Sanjit Hossain, & Sarder Abdulla Al Shiam. (2025). Advancing Neurological Disease Prediction through Machine Learning Techniques. Journal of Computer Science and Technology Studies, 7(1), 139-156. https://doi.org/10.32996/jcsts.2025.7.1.11

References

[1] Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020, baaa010.

[2] Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020, baaa010.

[3] Asadzadeh, S., Rezaii, T. Y., Beheshti, S., Delpak, A., & Meshgini, S. (2020). A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities. Journal of neuroscience methods, 339, 108740.

[4] Boateng, E. Y., Otoo, J., & Abaye, D. A. (2020). Basic tenets of classification algorithms K-nearest-neighbor, support vector machine, random forest and neural network: A review. Journal of Data Analysis and Information Processing, 8(4), 341-357.

[5] Boot, E., Butcher, N. J., Udow, S., Marras, C., Mok, K. Y., Kaneko, S., ... & International Research Group on 22q11. 2DS-associated Parkinson's Disease. (2018). Typical features of Parkinson disease and diagnostic challenges with microdeletion 22q11. 2. Neurology, 90(23), e2059-e2067.

[6] Dziak, J. J., Coffman, D. L., Lanza, S. T., Li, R., & Jermiin, L. S. (2020). Sensitivity and specificity of information criteria. Briefings in bioinformatics, 21(2), 553-565.

[7] Gao, C., Sun, H., Wang, T., Tang, M., Bohnen, N. I., Müller, M. L., ... & Dinov, I. D. (2018). Model-based and model-free machine learning techniques for diagnostic prediction and classification of clinical outcomes in Parkinson’s disease. Scientific reports, 8(1), 7129.

[8] Garcia Santa Cruz, B., Husch, A., & Hertel, F. (2023). Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions. Frontiers in Aging Neuroscience, 15, 1216163.

[9] Goriparthi, R. G. (2022). Interpretable Machine Learning Models for Healthcare Diagnostics: Addressing the Black-Box Problem. Revista de Inteligencia Artificial en Medicina, 13(1), 508-534.

[10] Gregorutti, B., Michel, B., & Saint-Pierre, P. (2017). Correlation and variable importance in random forests. Statistics and Computing, 27, 659-678.

[11] Halder, R. K., Uddin, M. N., Uddin, M. A., Aryal, S., & Khraisat, A. (2024). Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications. Journal of Big Data, 11(1), 113.

[12] Hicks, S. A., Strümke, I., Thambawita, V., Hammou, M., Riegler, M. A., Halvorsen, P., & Parasa, S. (2022). On evaluation metrics for medical applications of artificial intelligence. Scientific reports, 12(1), 5979.

[13] Król-Grzymała, A., Sienkiewicz-Szłapka, E., Fiedorowicz, E., Rozmus, D., Cieślińska, A., & Grzybowski, A. (2022). Tear biomarkers in Alzheimer’s and Parkinson’s diseases, and multiple sclerosis: implications for diagnosis (systematic review). International Journal of Molecular Sciences, 23(17), 10123.

[14] Lima, A. A., Mridha, M. F., Das, S. C., Kabir, M. M., Islam, M. R., & Watanobe, Y. (2022). A comprehensive survey on the detection, classification, and challenges of neurological disorders. Biology, 11(3), 469.

[15] Lima, A. A., Mridha, M. F., Das, S. C., Kabir, M. M., Islam, M. R., & Watanobe, Y. (2022). A comprehensive survey on the detection, classification, and challenges of neurological disorders. Biology, 11(3), 469.

[16] Liu, W., Fan, H., & Xia, M. (2022). Credit scoring based on tree-enhanced gradient boosting decision trees. Expert Systems with Applications, 189, 116034.

[17] Magyari, M., & Sorensen, P. S. (2020). Comorbidity in multiple sclerosis. Frontiers in Neurology, 11, 851.

[18] Mukherjee, S., Ali, S., Hashmi, S., & Jahan, S. (2023). History, Origin and Types of Neurological Disorders. In Applications of Stem Cells and derived Exosomes in Neurodegenerative Disorders (pp. 1-32). Singapore: Springer Nature Singapore.

[19] Myszczynska, M. A., Ojamies, P. N., Lacoste, A. M., Neil, D., Saffari, A., Mead, R., ... & Ferraiuolo, L. (2020). Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nature reviews neurology, 16(8), 440-456.

[20] Noor, M. B. T., Zenia, N. Z., Kaiser, M. S., Mamun, S. A., & Mahmud, M. (2020). Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia. Brain informatics, 7, 1-21.

[21] Novaković, J. D., Veljović, A., Ilić, S. S., Papić, Ž., & Tomović, M. (2017). Evaluation of classification models in machine learning. Theory and Applications of Mathematics & Computer Science, 7(1), 39.

[22] Nowrozy, R., Ahmed, K., Wang, H., & Mcintosh, T. (2023, July). Towards a universal privacy model for electronic health record systems: an ontology and machine learning approach. In Informatics (Vol. 10, No. 3, p. 60). MDPI.

[23] Rasool, S., Husnain, A., Saeed, A., Gill, A. Y., & Hussain, H. K. (2023). Harnessing predictive power: exploring the crucial role of machine learning in early disease detection. JURIHUM: Jurnal Inovasi dan Humaniora, 1(2), 302-315.

[24] Roy, S., Meena, T., & Lim, S. J. (2022). Demystifying supervised learning in healthcare 4.0: A new reality of transforming diagnostic medicine. Diagnostics, 12(10), 2549.

[25] Senders, J. T., Staples, P. C., Karhade, A. V., Zaki, M. M., Gormley, W. B., Broekman, M. L., ... & Arnaout, O. (2018). Machine learning and neurosurgical outcome prediction: a systematic review. World neurosurgery, 109, 476-486.

[26] Shaffi, N., Vimbi, V., Mahmud, M., Subramanian, K., & Hajamohideen, F. (2023, August). Bagging the best: a hybrid SVM-KNN ensemble for accurate and early detection of Alzheimer’s and Parkinson’s diseases. In International Conference on Brain Informatics (pp. 443-455). Cham: Springer Nature Switzerland.

[27] Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M. A., Shambour, M. K. Y., Alsalibi, A. I., & Gandomi, A. H. (2022). Machine learning in medical applications: A review of state-of-the-art methods. Computers in Biology and Medicine, 145, 105458.

[28] Sutradhar, P., Tarefder, P. K., Prodan, I., Saddi, M. S., & Rozario, V. S. (2021). Multi-modal case study on MRI brain tumor detection using support vector machine, random forest, decision tree, K-nearest neighbor, temporal convolution & transfer learning. AIUB Journal of Science and Engineering (AJSE), 20(3), 107-117.

[29] Trevethan, R. (2017). Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice. Frontiers in public health, 5, 307.

[30] Vieira, S., Pinaya, W. H., & Mechelli, A. (2017). Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience & Biobehavioral Reviews, 74, 58-75.

[31] Wang, W., Lee, J., Harrou, F., & Sun, Y. (2020). Early detection of Parkinson’s disease using deep learning and machine learning. IEEE Access, 8, 147635-147646.

[32] Watts, D., Pulice, R. F., Reilly, J., Brunoni, A. R., Kapczinski, F., & Passos, I. C. (2022). Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis. Translational psychiatry, 12(1), 332.

[33] Xie, Y., Jiang, B., Gong, E., Li, Y., Zhu, G., Michel, P., ... & Zaharchuk, G. (2019). Use of gradient boosting machine learning to predict patient outcome in acute ischemic stroke on the basis of imaging, demographic, and clinical information. American Journal of Roentgenology, 212(1), 44-51.

[34] Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., ... & Pei, Y. (2024). A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis. Bioengineering, 11(3), 219.

[35] Yadav, S., & Singh, M. K. (2021). Hybrid machine learning classifier and ensemble techniques to detect Parkinson’s disease patients. SN Computer Science, 2, 1-10.

[36] Zuhair, V., Babar, A., Ali, R., Oduoye, M. O., Noor, Z., Chris, K., ... & Rehman, L. U. (2024). Exploring the impact of artificial intelligence on global health and enhancing healthcare in developing nations. Journal of Primary Care & Community Health, 15, 21501319241245847.

[37] Abir, S. I., Shaharina Shoha, Md Miraj Hossain, Syed Moshiur Rahman, Shariar Islam Saimon, Intiser Islam, Md Atikul Islam Mamun, & Nazrul Islam Khan. (2024). Deep Learning-Based Classification of Skin Lesions: Enhancing Melanoma Detection through Automated Preprocessing and Data Augmentation. Journal of Computer Science and Technology Studies, 6(5), 152-167. https://doi.org/10.32996/jcsts.2024.6.5.13

[38] Shaharina Shoha, Abir, S. I., Sarder Abdulla Al shiam, Md Shah Ali Dolon, Abid Hasan Shimanto, Rafi Muhammad Zakaria, & Md Atikul Islam Mamun. (2024). Enhanced Parkinson’s Disease Detection Using Advanced Vocal Features and Machine Learning .Journal of Computer Science and Technology Studies,6(5), 113–128. https://doi.org/10.32996/jcsts.2024.6.5.10

[39] Abir, Shake Ibna and Shoha, Shaharina and Dolon, Md Shah Ali and Al Shiam, Sarder Abdulla and Shimanto, Abid Hasan and Zakaria, Rafi Muhammad and Ridwan, Mohammad, “Lung Cancer Predictive Analysis Using Optimized Ensemble and Hybrid Machine Learning Techniques”. Available at SSRN: https://ssrn.com/abstract=4998936or http://dx.doi.org/10.2139/ssrn.4998936

[40] S. I. Abir, S. Shoha, S. A. Al Shiam, M. M. Uddin, M. A. Islam Mamun and S. M. Shamsul Arefeen, "A Comprehensive Examination of MR Image-Based Brain Tumor Detection via Deep Learning Networks," 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS), Marrakech, Morocco, 2024, pp. 1-8, https://doi.10.1109/ICDS62089.2024.10756457

[41] S. I. Abir, S. Shoha, S. A. Al Shiam,M. M. Uddin, M. A. Islam Mamun and S. M. Shamsul Arefeen, "Health Risks and Disease Transmission in Undocumented Immigrants in the U.S Using Predictive ML,"2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS), Marrakech, Morocco, 2024, pp. 1-6, https://doi.10.1109/ICDS62089.2024.10756308

[42] Abir, Shake Ibna, Richard Schugart, (2024). “Parameter Estimation for Stroke Patients Using Brain CT Perfusion Imaging with Deep Temporal Convolutional Neural Network”, Masters Theses & Specialist Projects, Paper 3755.

[43] Sohail, M. N., Ren, J., Muhammad, M. U., Rizwan, T., Iqbal, W., Abir, S. I., and Bilal, M, (2019). Group covariates assessment on real life diabetes patients by fractional polynomials: a study based on logistic regression modeling, Journal of Biotech Research, 10, 116-125.

[44] Sohail, M. N., Jiadong, R., Irshad, M., Uba, M. M., and Abir, S. I, (2018). Data mining techniques for Medical Growth: A Contribution of Researcher reviews, Int. J. Comput. Sci. Netw. Secur, 18, 5-10.

[45] Sohail, M. N., Ren, J. D., Uba, M. M., Irshad, M. I., Musavir, B., Abir, S. I., and Anthony, J. V, (2018). Why only data mining? a pilot study on inadequacy and domination of data mining technology, Int. J. Recent Sci. Res, 9(10), 29066-29073

[46] Abir, S. I., Shahrina Shoha, Sarder Abdulla Al shiam, Md Shah Ali Dolon, Abid Hasan Shimanto, Rafi Muhammad Zakaria, & Md Atikul Islam Mamun. (2024). Deep Neural Networks in Medical Imaging: Advances, Challenges, and Future Directions for Precision Healthcare. Journal of Computer Science and Technology Studies, 6(5), 94-112. https://doi.org/10.32996/jcsts.2024.6.5.9

[47] Abir, S. I., Shaharina Shoha, Sarder Abdulla Al Shiam, Shariar Islam Saimon, Intiser Islam, Md Atikul Islam Mamun, Md Miraj Hossain, Syed Moshiur Rahman, & Nazrul Islam Khan. (2024). Precision Lesion Analysis and Classification in Dermatological Imaging through Advanced Convolutional Architectures. Journal of Computer Science and Technology Studies, 6(5), 168-180.

[48] Abir, S. I., Shaharina Shoha, Sarder Abdulla Al shiam, Nazrul Islam Khan, Abid Hasan Shimanto, Muhammad Zakaria, & S M Shamsul Arefeen. (2024). Deep Learning Application of LSTM(P) to predict the risk factors of etiology cardiovascular disease. Journal of Computer Science and Technology Studies, 6(5), 181-200. https://doi.org/10.32996/jcsts.2024.6.5.15

[49] Akhter, A., Sarder Abdulla Al Shiam, Mohammad Ridwan, Abir, S. I., Shoha, S., Nayeem, M. B., … Robeena Bibi. (2024). Assessing the Impact of Private Investment in AI and Financial Globalization on Load Capacity Factor: Evidence from United States. Journal of Environmental Science and Economics, 3(3), 99–127. https://doi.org/10.56556/jescae.v3i3.977

[50] Hossain, M. S., Mohammad Ridwan, Akhter, A., Nayeem, M. B., M Tazwar Hossain Choudhury, Asrafuzzaman, M., … Sumaira. (2024). Exploring the LCC Hypothesis in the Nordic Region: The Role of AI Innovation, Environmental Taxes, and Financial Accessibility via Panel ARDL. Global Sustainability Research , 3(3), 54–80. https://doi.org/10.56556/gssr.v3i3.972

[51] Shewly Bala, Abdulla Al Shiam, S., Shamsul Arefeen, S. M., Abir, S. I., Hemel Hossain, Hossain, M. S., … Sumaira. (2024). Measuring How AI Innovations and Financial Accessibility Influence Environmental Sustainability in the G-7: The Role of Globalization with Panel ARDL and Quantile Regression Analysis. Global Sustainability Research, 3(4), 1–29. https://doi.org/10.56556/gssr.v3i4.974

[52] Abir, S. I., Shoha, S., Abdulla Al Shiam, S., Dolon, M. S. A., Shewly Bala, Hemel Hossain, … Robeena Bibi. (2024). Enhancing Load Capacity Factor: The Influence of Financial Accessibility, AI Innovation, and Institutional Quality in the United States. Journal of Environmental Science and Economics, 3(4), 12–36. https://doi.org/10.56556/jescae.v3i4.979

[53] Abdulla Al Shiam, S., Mohammad Ridwan, Mahdi Hasan, M., Akhter, A., Shamsul Arefeen, S. M., Hossain, M. S., … Shoha, S. (2024). Analyzing the Nexus between AI Innovation and Ecological Footprint in Nordic Region: Impact of Banking Development and Stock Market Capitalization using Panel ARDL method. Journal of Environmental Science and Economics, 3(3), 41–68. https://doi.org/10.56556/jescae.v3i3.973

[54] Mohammad Ridwan, Bala, S., Abdulla Al Shiam, S., Akhter, A., Mahdi Hasan, M., Asrafuzzaman, M., … Bibi, R. (2024). Leveraging AI for Promoting Sustainable Environments in G-7: The Impact of Financial Development and Digital Economy via MMQR Approach. Global Sustainability Research , 3(3), 27–53. https://doi.org/10.56556/gssr.v3i3.971

[55] Mohammad Ridwan, Bala, S., Shiam, S. A. A., Akhter, A., Asrafuzzaman, M., Shochona, S. A., … Shoha, S. (2024). Leveraging AI for a Greener Future: Exploring the Economic and Financial Impacts on Sustainable Environment in the United States. Journal of Environmental Science and Economics, 3(3), 1–30. https://doi.org/10.56556/jescae.v3i3.970

[56] Abdulla Al Shiam, S., Abir, S. I., Dipankar Saha, Shoha, S., Hemel Hossain, Dolon, M. S. A. Mohammad Ridwan. (2024). Assessing the Impact of AI Innovation, Financial Development, and the Digital Economy on Load Capacity Factor in the BRICS Region. Journal of Environmental Science and Economics, 3(2), 102–126. https://doi.org/10.56556/jescae.v3i2.981

[57] Shoha, S., Abdulla Al Shiam, S., Abir, S. I., Dipankar Saha, Shewly Bala, Dolon, M. S. A., … Robeena Bibi. (2024). Towards Carbon Neutrality: The Impact of Private AI Investment and Financial Development in the United States – An Empirical Study Using the STIRPAT Model. Journal of Environmental Science and Economics, 3(4), 59–79. https://doi.org/10.56556/jescae.v3i4.982

[58] Abir, S. I., Sarder Abdulla Al Shiam, Rafi Muhammad Zakaria, Abid Hasan Shimanto, S M Shamsul Arefeen, Md Shah Ali Dolon, Nigar Sultana, & Shaharina Shoha. (2024). Use of AI-Powered Precision in Machine Learning Models for Real-Time Currency Exchange Rate Forecasting in BRICS Economies. Journal of Economics, Finance and Accounting Studies, 6(6), 66-83. https://doi.org/10.32996/jefas.2024.6.6.6

[59] Abdulla Al Shiam, S., Abir, S. I., Dipankar Saha, Shoha, S., Hemel Hossain, Dolon, M. S. A., Mohammad Ridwan. (2024). Assessing the Impact of AI Innovation, Financial Development, and the Digital Economy on Load Capacity Factor in the BRICS Region. Journal of Environmental Science and Economics, 3(2), 102–126. https://doi.org/10.56556/jescae.v3i2.981

[60] Mohammad Ridwan, Abdulla Al Shiam, S., Hemel Hossain, Abir, S. I., Shoha, S., Dolon, M. S. A., … Rahman, H. (2024). Navigating a Greener Future: The Role of Geopolitical Risk, Financial Inclusion, and AI Innovation in the BRICS – An Empirical Analysis. Journal of Environmental Science and Economics, 3(1), 78–103. https://doi.org/10.56556/jescae.v3i1.980

[61] Nigar Sultana, Shaharina Shoha, Md Shah Ali Dolon, Sarder Abdulla Al Shiam, Rafi Muhammad Zakaria, Abid Hasan Shimanto, S M Shamsul Arefeen, & Abir, S. I. (2024). Machine Learning Solutions for Predicting Stock Trends in BRICS amid Global Economic Shifts and Decoding Market Dynamics. Journal of Economics, Finance and Accounting Studies, 6(6), 84-101. https://doi.org/10.32996/jefas.2024.6.6.7

[62] Abir, S. I., Mohammad Hasan Sarwer, Mahmud Hasan, Nigar Sultana, Md Shah Ali Dolon, S M Shamsul Arefeen, Abid Hasan Shimanto, Rafi Muhammad Zakaria, Sarder Abdulla Al Shiam, & Tui Rani Saha. (2024). Accelerating BRICS Economic Growth: AI-Driven Data Analytics for Informed Policy and Decision Making. Journal of Economics, Finance and Accounting Studies , 6(6), 102-115. https://doi.org/10.32996/jefas.2024.6.6.8

[63] Shoha, Shaharina, "A Comparison of Computational Perfusion Imaging Techniques" (2023). Masters Theses & Specialist Projects. Paper 3680.https://digitalcommons.wku.edu/theses/3680

[64] Abir, S. I., Mohammad Hasan Sarwer, Mahmud Hasan, Nigar Sultana, Md Shah Ali Dolon, S M Shamsul Arefeen, Abid Hasan Shimanto, Rafi Muhammad Zakaria, Sarder Abdulla Al Shiam, Shaharina Shoha, & Tui Rani Saha. (2025). Deep Learning for Financial Markets: A Case-Based Analysis of BRICS Nations in the Era of Intelligent Forecasting. Journal of Economics, Finance and Accounting Studies , 7(1), 01-15. https://doi.org/10.32996/jefas.2025.7.1.1

[65] Abir, S. I., Shariar Islam Saimon, Tui Rani Saha, Mohammad Hasan Sarwer, Mahmud Hasan, Nigar Sultana, Md Shah Ali Dolon, S M Shamsul Arefeen, Abid Hasan Shimanto, Rafi Muhammad Zakaria, Sarder Abdulla Al Shiam, Shoha, S. ., & Intiser Islam. (2025). Comparative Analysis of Currency Exchange and Stock Markets in BRICS Using Machine Learning to Forecast Optimal Trends for Data-Driven Decision Making. Journal of Economics, Finance and Accounting Studies , 7(1), 26-48. https://doi.org/10.32996/jefas.2025.7.1.3

[66] Abir, S. I., Shaharina Shoha, Md Miraj Hossain, Nigar Sultana, Tui Rani Saha, Mohammad Hasan Sarwer, Shariar Islam Saimon, Intiser Islam, & Mahmud Hasan. (2025). Machine Learning and Deep Learning Techniques for EEG-Based Prediction of Psychiatric Disorders. Journal of Computer Science and Technology Studies, 7(1), 46-63. https://doi.org/10.32996/jcsts.2025.7.1.4

[67] Mohammad Hasan Sarwer, Tui Rani Saha, Abir, S. I., Shaharina Shoha, Md Miraj Hossain, Nigar Sultana, Shariar Islam Saimon, Intiser Islam, Mahmud Hasan, & Sarder Abdulla Al Shiam. (2025). EEG Functional Connectivity and Deep Learning for Automated Diagnosis of Alzheimer’s disease and Schizophrenia. Journal of Computer Science and Technology Studies, 7(1), 82-99. https://doi.org/10.32996/jcsts.2025.7.1.7

Downloads

Views

78

Downloads

42

Keywords:

Machine learning, neurological diseases, data preprocessing, feature engineering, Gradient Boosting, EEG analysis, sensitivity, specificity, predictive modeling, clinical diagnostics.