A Comprehensive Investigation into Detecting Schizophrenia from  EEG Signals Using a Machine Learning Approach

Akib, A.Abdur Rahman; Zaman, S M Mehedi; Farzana, Fabiha

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dc.contributor.author	Akib, A.Abdur Rahman
dc.contributor.author	Zaman, S M Mehedi
dc.contributor.author	Farzana, Fabiha
dc.date.accessioned	2024-01-16T09:15:37Z
dc.date.available	2024-01-16T09:15:37Z
dc.date.issued	2023-06-30
dc.identifier.citation	[1] Häfner, Heinz. “Psychische Krankheit – Ein Mehrregionenbegriff.” Fortschritte Der Neurologie · Psychiatrie, vol. 87, no. 12, 17 Dec. 2018, pp. 685–694, https://doi.org/10.1055/a 0624-9456. [2] ---. “World Mental Health Report: Transforming Mental Health for All.” Www.who.int, 16 June 2022, www.who.int/publications/i/item/9789240049338. [3] Institute for Health Metrics and Evaluation. “GBD Results.” Institute for Health Metrics and Evaluation, University of Washington, 2019, vizhub.healthdata.org/gbd-results/. [4] World Health Organization. “Mental Health and COVID-19: Early Evidence of the Pandemic’s Impact: Scientific Brief, 2 March 2022.” Www.who.int, 2 Mar. 2022, www.who.int/publications/i/item/WHO-2019-nCoV-Sci_Brief-Mental_health-2022.1. [5] Telles-Correia, Diogo, et al. “Mental Disorder—the Need for an Accurate Definition.” Frontiers in Psychiatry, vol. 9, no. 64, 12 Mar. 2018, www.ncbi.nlm.nih.gov/pmc/articles/PMC5857571/, https://doi.org/10.3389/fpsyt.2018.00064. [6] World Health Organization. “Mental Disorders.” World Health Organization, 8 June 2022, www.who.int/news-room/fact-sheets/detail/mental-disorders. [7] ---. “Schizophrenia.” Nature Reviews Disease Primers, vol. 1, no. 1, 12 Nov. 2015, www.nature.com/articles/nrdp201567, https://doi.org/10.1038/nrdp.2015.67. [8] Freedman, Robert. “Schizophrenia.” New England Journal of Medicine, vol. 349, no. 18, 30 Oct. 2003, pp. 1738–1749, https://doi.org/10.1056/nejmra035458. [9] Mueser, K.T., Salyers, M.P. and Mueser, P.R., 2001. A prospective analysis of work in schizophrenia. Schizophrenia bulletin, 27(2), pp.281-296. 45 \| Page [10] “Schizophrenia - Symptoms and Causes.” Mayo Clinic, 7 Jan. 2020, www.mayoclinic.org/diseases-conditions/schizophrenia/symptoms-causes/syc 20354443#:~:text=Overview. [11] “GBD Results Tool \| GHDx.” Ghdx.healthdata.org, ghdx.healthdata.org/gbd-results tool?params=gbd-api-2019-permalink/27a7644e8ad28e739382d31e77589dd7. [12] Armstrong, Este, et al. “The Ontogeny of Human Gyrification.” Cerebral Cortex, vol. 5, no. 1, 1995, pp. 56–63, https://doi.org/10.1093/cercor/5.1.56. Accessed 16 Oct. 2021. [13] Andreasen, Nancy C. “Understanding the Causes of Schizophrenia.” New England Journal of Medicine, vol. 340, no. 8, 25 Feb. 1999, pp. 645–647, www.nejm.org/doi/full/10.1056/NEJM199902253400811, https://doi.org/10.1056/nejm199902253400811. [14] Hussien, Zebiba Nassir. “Prevalence and Associate Factors of Suicidal Ideation and Attempt among People with Schizophrenia at Amanuel Mental Specialized Hospital Addis Ababa, Ethiopia.” Journal of Psychiatry, vol. 18, no. 1, 2015, https://doi.org/10.4172/psychiatry.1000184. Accessed 25 Aug. 2019. [15] Rasool, Shahid, et al. “Schizophrenia: An Overview.” Clinical Practice, vol. 15, no. 5, 2018, https://doi.org/10.4172/clinical-practice.1000417. [16] ---. “A National Study of Violent Behavior in Persons with Schizophrenia.” Archives of General Psychiatry, vol. 63, no. 5, 1 May 2006, p. 490, jamanetwork.com/journals/jamapsychiatry/fullarticle/209569, https://doi.org/10.1001/archpsyc.63.5.490. Accessed 22 Oct. 2019. [17] Zhang, Lei. “EEG Signals Classification Using Machine Learning for the Identification and Diagnosis of Schizophrenia.” 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 2019, https://doi.org/10.1109/embc.2019.8857946. 46 \| Page [18] Vincent, J.-L. and Creteur, J. (2019). Critical care medicine in 2050: less invasive, more connected, and personalized. Journal of Thoracic Disease, 11(1), pp.335–338. doi:https://doi.org/10.21037/jtd.2018.11.66. [19] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S. and Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), pp.24–29. doi:https://doi.org/10.1038/s41591-018-0316-z. [20] Blinowska, Katarzyna, and Piotr Durka. “Electroencephalography (EEG).” Wiley Encyclopedia of Biomedical Engineering, 14 Apr. 2006, https://doi.org/10.1002/9780471740360.ebs0418. [21] Gordillo, Darío, et al. The EEG Multiverse of Schizophrenia. Vol. 33, no. 7, 27 Aug. 2022, pp. 3816–3826, https://doi.org/10.1093/cercor/bhac309. Accessed 4 June 2023 [22] S. L. Oh et al., “A deep learning approach for Parkinson’s disease diagnosis from EEG signals,” Neural Comput & Applic, vol. 32, no. 15, pp. 10927–10933, Aug. 2020, doi: 10.1007/s00521-018-3689-5. [23] U. R. Acharya et al., “A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals,” European Neurology, vol. 74, no. 1–2, pp. 79–83, Aug. 2015, doi: 10.1159/000438457. [24] Y. Zhu et al., “Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study,” Schizophrenia Bulletin, vol. 48, no. 3, pp. 563–574, May 2022, doi: 10.1093/schbul/sbac030. [25] A. Shoeibi et al., “Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN LSTM Models,” Frontiers in Neuroinformatics, vol. 15, 2021, Accessed: May 27, 2023. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fninf.2021.777977 [26] L. Zhang, “EEG Signals Classification Using Machine Learning for The Identification and Diagnosis of Schizophrenia,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul. 2019, pp. 4521–4524. doi: 10.1109/EMBC.2019.8857946. 47 \| Page [27] D.-W. Ko and J.-J. Yang, “EEG-Based Schizophrenia Diagnosis through Time Series Image Conversion and Deep Learning,” Electronics, vol. 11, no. 14, Art. no. 14, Jan. 2022, doi: 10.3390/electronics11142265. [28] J. Oh, B.-L. Oh, K.-U. Lee, J.-H. Chae, and K. Yun, “Identifying Schizophrenia Using Structural MRI With a Deep Learning Algorithm,” Frontiers in Psychiatry, vol. 11, 2020, Accessed: May 27, 2023. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fpsyt.2020.00016 [29] G. S. Chilla, L. Y. Yeow, Q. H. Chew, K. Sim, and K. N. B. Prakash, “Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods,” Sci Rep, vol. 12, no. 1, Art. no. 1, Feb. 2022, doi: 10.1038/s41598-022-06651-4. [30] J. Ruiz de Miras, A. J. Ibáñez-Molina, M. F. Soriano, and S. Iglesias-Parro, “Schizophrenia classification using machine learning on resting state EEG signal,” Biomedical Signal Processing and Control, vol. 79, p. 104233, Jan. 2023, doi: 10.1016/j.bspc.2022.104233. [31] K. Desai, “Using Electroencephalographic Signal Processing and Machine Learning Binary Classification to diagnose Schizophrenia,” In Review, preprint, Mar. 2023. doi: 10.21203/rs.3.rs-2715657/v1. [32] Y. Xiao et al., “Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI,” Schizophrenia Research, vol. 214, pp. 11–17, Dec. 2019, doi: 10.1016/j.schres.2017.11.037. [33] Laton, J.; Van Schependom, J.; Gielen, J.; Decoster, J.; Moons, T.; De Keyser, J.; De Hert, M.; Nagels, G. Single-subject classification of schizophrenia patients based on a combination of oddball and mismatch evoked potential paradigms. J. Neurol. Sci. 2014, 347, 262–267. [Google Scholar] [CrossRef] [37] Laton, J.; Van Schependom, J.; Gielen, J.; Decoster, J.; Moons, T.; De Keyser, J.; De Hert, M.; Nagels, G. Single-subject classification of schizophrenia patients based on a combination of oddball and mismatch evoked potential paradigms. J. Neurol. Sci. 2014, 347, 262–267. [35] Neuhaus, A.H.; Popescu, F.C.; Rentzsch, J.; Gallinat, J. Critical evaluation of auditory event-related potential deficits in schizophrenia: Evidence from large-scale single-subject pattern classification. Schizophr. Bull. 2014, 40, 1062–1071. [36] Johannesen, J.K.; Bi, J.; Jiang, R.; Kenney, J.G.; Chen, C.M.A. Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults. Neuropsychiatr. Electrophysiol. 2016, 2, 3–21. [36] Shim, M.; Hwang, H.J.; Kim, D.W.; Lee, S.H.; Im, C.H. Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features. Schizophr. Res. 48 \| Page 2016, 176, 314–319. [38] Taylor, J.A.; Matthews, N.; Michie, P.T.; Rosa, M.J.; Garrido, M.I. Auditory prediction errors as individual biomarkers of schizophrenia. NeuroImage Clin. 2017, 15, 264–273. [39] Krishnan, P.T.; Raj, A.N.J.; Balasubramanian, P.; Chen, Y. Schizophrenia detection using Multivariate Empirical Mode Decomposition and Entropy Measures from Multichannel EEG Sentropy measures from multichannel EEG signal. Biocybern. Biomed. Eng. 2020, 40, 1124– 1139. [40] Schnack, H.G.; Nieuwenhuis, M.; van Haren, N.E.; Abramovic, L.; Scheewe, T.W.; Brouwer, R.M.; Pol, H.E.H.; Kahn, R.S. Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects. Neuroimage 2014, 84, 299–306. [41] Cabral, C.; Kambeitz-Ilankovic, L.; Kambeitz, J.; Calhoun, V.D.; Dwyer, D.B.; Von Saldern, S.; Urquijo, M.F.; Falkai, P.; Koutsouleris, N. Classifying schizophrenia using multimodal multivariate pattern recognition analysis: Evaluating the impact of individual clinical profiles on the neurodiagnostic performance. Schizophr. Bull. 2016, 42, S110–S117. [42] Lu, X.; Yang, Y.; Wu, F.; Gao, M.; Xu, Y.; Zhang, Y.; Yao, Y.; Du, X.; Li, C.; Wu, L.; et al. Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images. Medicine (Baltimore) 2016, 95, e3973. [43] Squarcina, L.; Castellani, U.; Bellani, M.; Perlini, C.; Lasalvia, A.; Dusi, N.; Bonetto, C.; Cristofalo, D.; Tosato, S.; Rambaldelli, G.; et al. Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques. Neuroimage 2017, 145, 238–245. [44] Rozycki, M.; Satterthwaite, T.D.; Koutsouleris, N.; Erus, G.; Doshi, J.; Wolf, D.H.; Fan, Y.; Gur, R.E.; Gur, R.C.; Meisenzahl, E.M.; et al. Multisite machine learning analysis provides a robust structural imaging signature of schizophrenia detectable across diverse patient populations and within individuals. Schizophr. Bull. 2018, 44, 1035–1044. [45] de Moura, A.M.; Pinaya, W.H.L.; Gadelha, A.; Zugman, A.; Noto, C.; Cordeiro, Q.; Belangero, S.I.; Jackowski, A.P.; Bressan, R.A.; Sato, J.R. Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach. Psychiatry Res. Neuroimaging 2018, 275, 14–20. [46] Liang, S.; Li, Y.; Zhang, Z.; Kong, X.; Wang, Q.; Deng, W.; Li, X.; Zhao, L.; Li, M.; Meng, Y.; et al. Classification of first-episode schizophrenia using multimodal brain features: A combined structural and diffusion imaging study. Schizophr. Bull. 2019, 45, 591–599. 49 \| Page [47] Deng, Y.; Hung, K.S.; Lui, S.S.; Chui, W.W.; Lee, J.C.; Wang, Y.; Li, Z.; Mak, H.K.; Sham, P.C.; Chan, R.C.; et al. Tractography-based classification in distinguishing patients with first-episode schizophrenia from healthy individuals. Prog. Neuropsychopharmacol. Biol. Psychiatry 2019, 88, 66–73. [48] Mikolas, P.; Melicher, T.; Skoch, A.; Matejka, M.; Slovakova, A.; Bakstein, E.; Hajek, T.; Spaniel, F. Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: A machine-learning study. Psychol. Med. 2016, 46, 2695–2704. [49] Peters, H.; Shao, J.; Scherr, M.; Schwerthöffer, D.; Zimmer, C.; Förstl, H.; Bäuml, J.; Wohlschläger, A.; Riedl, V.; Koch, K.; et al. More consistently altered connectivity patterns for cerebellum and medial temporal lobes than for amygdala and striatum in schizophrenia. Front. Hum. Neurosci. 2016, 10, 55. [50] Skåtun, K.C.; Kaufmann, T.; Doan, N.T.; Alnæs, D.; Córdova-Palomera, A.; Jönsson, E.G.; Fatouros-Bergman, H.; Flyckt, L.; KaSP; Melle, I.; et al. Consistent functional connectivity alterations in schizophrenia spectrum disorder: A multisite study. Schizophr. Bull. 2017, 43, 914–924. [51] Yang, H.; He, H.; Zhong, J. Multimodal MRI characterisation of schizophrenia: A discriminative analysis. Lancet 2016, 388, S36. [52] Chen, X.; Liu, C.; He, H.; Chang, X.; Jiang, Y.; Li, Y.; Duan, M.; Li, J.; Luo, C.; Yao, D. Transdiagnostic differences in the resting-state functional connectivity of the prefrontal cortex in depression and schizophrenia. J. Affect. Disord. 2017, 217, 118–124. [53] Kaufmann, T.; Alnæs, D.; Brandt, C.L.; Doan, N.T.; Kauppi, K.; Bettella, F.; Lagerberg, T.V.; Berg, A.O.; Djurovic, S.; Agartz, I.; et al. Task modulations and clinical manifestations in the brain functional connectome in 1615 fMRI datasets. Neuroimage 2017, 147, 243–252. [54] Guo, W.; Liu, F.; Chen, J.; Wu, R.; Li, L.; Zhang, Z.; Zhao, J. Family-based case-control study of homotopic connectivity in first-episode, drug-naive schizophrenia at rest. Sci. Rep. 2017, 7, 43312. [55] Iwabuchi, S.J.; Palaniyappan, L. Abnormalities in the effective connectivity of visuothalamic circuitry in schizophrenia. Psychol. Med. 2017, 47, 1300–1310. [56] Yang, Y.; Cui, Y.; Xu, K.; Liu, B.; Song, M.; Chen, J.; Wang, H.; Chen, Y.; Guo, H.; Li, P.; et al. Distributed functional connectivity impairment in schizophrenia: A multi-site study. In Proceedings of the 2nd IET International Conference on Biomedical Image and Signal Processing (ICBISP 2017), Wuhan, China, 13–14 May 2017; IET: London, UK, 2017; pp. 1–6. 50 \| Page [57] Bae, Y.; Kumarasamy, K.; Ali, I.M.; Korfiatis, P.; Akkus, Z.; Erickson, B.J. Differences between schizophrenic and normal subjects using network properties from fMRI. J. Digit. Imaging 2018, 31, 252–261. [58] Li, J.; Sun, Y.; Huang, Y.; Bezerianos, A.; Yu, R. Machine learning technique reveals intrinsic characteristics of schizophrenia: An alternative method. Brain Imaging Behav. 2019, 13, 1386–1396. [59] Chatterjee, I.; Kumar, V.; Sharma, S.; Dhingra, D.; Rana, B.; Agarwal, M.; Kumar, N. Identification of brain regions associated with working memory deficit in schizophrenia F1000Research 2019, 8, 124. [60] Kalmady, S.V.; Greiner, R.; Agrawal, R.; Shivakumar, V.; Narayanaswamy, J.C.; Brown, M.R.; Greenshaw, A.J.; Dursun, S.M.; Venkatasubramanian, G. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble learning. NPJ Schizophr. 2019, 5, 1–11. [61] Kirihara, K., Tada, M., Koshiyama, D., Fujioka, M., Usui, K., Araki, T. and Kasai, K., 2020. A predictive coding perspective on mismatch negativity impairment in schizophrenia. Frontiers in psychiatry, 11, p.660. [62] Gorbachevskaya, K. and Borisov, S., 2019. Eeg of healthy adolescents and adolescents with symptoms of schizophrenia. [63] Olejarczyk, E. and Jernajczyk, W., 2017. EEG in schizophrenia. RepOD. [64] Seglen, P.O., 1992. The skewness of science. Journal of the American society for information science, 43(9), pp.628-638. [65] Balanda, K.P. and MacGillivray, H.L., 1988. Kurtosis: a critical review. The American Statistician, 42(2), pp.111-119. [66] Mela, C.F. and Kopalle, P.K., 2002. The impact of collinearity on regression analysis: the asymmetric effect of negative and positive correlations. Applied Economics, 34(6), pp.667-677. 51 \| Page [67] Bentéjac, C., Csörgő, A. and Martínez-Muñoz, G., 2021. A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3), pp.1937-1967. [68] Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. and Gulin, A., 2018. CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems, 31. [69] Huang, G., Wu, L., Ma, X., Zhang, W., Fan, J., Yu, X., Zeng, W. and Zhou, H., 2019. Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions. Journal of Hydrology, 574, pp.1029-1041. [70] Luo, M., Wang, Y., Xie, Y., Zhou, L., Qiao, J., Qiu, S. and Sun, Y., 2021. Combination of feature selection and catboost for prediction: The first application to the estimation of aboveground biomass. Forests, 12(2), p.216. [71] Ogunleye, A. and Wang, Q.G., 2019. XGBoost model for chronic kidney disease diagnosis. IEEE/ACM transactions on computational biology and bioinformatics, 17(6), pp.2131-2140. [72] Dhaliwal, S.S., Nahid, A.A. and Abbas, R., 2018. Effective intrusion detection system using XGBoost. Information, 9(7), p.149. [73] Ren, X., Guo, H., Li, S., Wang, S. and Li, J., 2017. A novel image classification method with CNN-XGBoost model. In Digital Forensics and Watermarking: 16th International Workshop, IWDW 2017, Magdeburg, Germany, August 23-25, 2017, Proceedings 16 (pp. 378- 390). Springer International Publishing. [74] Al Daoud, E., 2019. Comparison between XGBoost, LightGBM and CatBoost using a home credit dataset. International Journal of Computer and Information Engineering, 13(1), pp.6-10. [75] Ju, Y., Sun, G., Chen, Q., Zhang, M., Zhu, H. and Rehman, M.U., 2019. A model combining convolutional neural network and LightGBM algorithm for ultra-short-term wind power forecasting. Ieee Access, 7, pp.28309-28318. [76] Wang, Y., Chen, J., Chen, X., Zeng, X., Kong, Y., Sun, S., Guo, Y. and Liu, Y., 2020. Short-term load forecasting for industrial customers based on TCN-LightGBM. IEEE 52 \| Page Transactions on Power Systems, 36(3), pp.1984-1997. [77] Liang, W., Luo, S., Zhao, G. and Wu, H., 2020. Predicting hard rock pillar stability using GBDT, XGBoost, and LightGBM algorithms. Mathematics, 8(5), p.765. [79] Baby, D., Devaraj, S.J. and Hemanth, J., 2021. Leukocyte classification based on feature selection using extra trees classifier: Atransfer learning approach. Turkish Journal of Electrical Engineering and Computer Sciences, 29(8), pp.2742-2757 . [80] Kaur, K. and Mittal, S.K., 2020. Classification of mammography image with CNN-RNN based semantic features and extra tree classifier approach using LSTM. Materials Today: Proceedings. [81] Sharma, D., Kumar, R. and Jain, A., 2022. Breast cancer prediction based on neural networks and extra tree classifier using feature ensemble learning. Measurement: Sensors, 24, p.100560. [82] Lusa, L., 2017. Gradient boosting for high-dimensional prediction of rare events. Computational Statistics & Data Analysis, 113, pp.19-37. [83] Bahad, P. and Saxena, P., 2020. Study of adaboost and gradient boosting algorithms for predictive analytics. In International Conference on Intelligent Computing and Smart Communication 2019: Proceedings of ICSC 2019 (pp. 235-244). Springer Singapore. [84] Punmiya, R. and Choe, S., 2019. Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing. IEEE Transactions on Smart Grid, 10(2), pp.2326-2329. [85] Akiba, T., Sano, S., Yanase, T., Ohta, T. and Koyama, M., 2019, July. Optuna: A next generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2623-2631). [86] Yacouby, R. and Axman, D., 2020, November. Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. In Proceedings of the first workshop on evaluation and comparison of NLP systems (pp. 79-91)	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/2032
dc.description	Supervised by Mr. Mirza Muntasir Nishat, Assistant Professor, Department of Electrical and Electronics Engineering (EEE) Islamic University of Technology (IUT) Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.description.abstract	Schizophrenia is a prevalent psychiatric condition that places significant clinical demands on both patients and their caregivers. An accurate and expeditious diagnosis is essential for the effective treatment of schizophrenia. In this regard, the identification of classification biomarkers has the potential to enhance comprehension of the neural underpinnings of schizophrenia and supplement clinical assessments. Recent years have seen an increase in research into the diagnostic and prognostic utility of Machine Learning (ML) techniques in schizophrenia. Several such studies have attempted to classify individuals with schizophrenia from healthy controls using neuroanatomical features. However, the range of neuroanatomical measures utilized in these investigations has been limited thus far. The objective of this study was to detect schizophrenia at an early stage using the largest EEG signal dataset to date, consisting of 193 patients. To compile this dataset, the three largest open-source EEG signal datasets were merged and processed. For the most accurate detection of schizophrenia from EEG signals, an ML array was utilized. With the Gradient Boosting Classifier (GBC) method, feature engineering, and model tuning, this research achieved one of the highest classification accuracies to date, 93.3%, among the other supervised ML models used in the study. In addition, the study’s results demonstrated that precision, recall, and f1 score were, respectively, 84.6%, 80%, and 82%. The obtained results from this thesis surpasses all previous works using EEG signal in terms of accuracy and number of subjects considered and the results were obtained only using supervised model which is computationally lighter than typical signal analyzing Convolutional Neural Network (CNN) models. This thesis concentrates on the robustness and significance of larger EEG signal datasets, as contemporary studies have implemented prediction strategies on relatively smaller datasets	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	A Comprehensive Investigation into Detecting Schizophrenia from EEG Signals Using a Machine Learning Approach	en_US
dc.type	Thesis	en_US