Increasing Schizophrenia Prediction Performance Using Advanced Deep Learning Methodologies
Keywords:
Explainable AI, recurrent neural networks, deep learning, schizophrenia prediction.Abstract
Predicting schizophrenia is a difficult task that can tremendously benefit from machine learning. In this study, it is suggested a thorough technique that includes data gathering, pre-processing, model building, training, and evaluation. Convolutional neural networks (CNNs), Deep Neural Networks (DNNs), and Recurrent Neural Networks (RNNs) are three Deep Learning architectures that were investigated for their ability to predict schizophrenia. The findings indicated that RNNs are suitable for capturing temporal dependencies in patient data, with the best accuracy rate of 0.87 being achieved in proposed study. Additionally, DNNs with more extensive training data have greater development potential, while CNNs perform competitively according to F1-Scores. CNNs regularly have higher precision values, demonstrating their dependability in reducing false positives. This research's future focus is on validation and optimisation, which will guarantee its robustness for clinical application. Interpretability can be improved by incorporating explainable AI (XAI) approaches. Beyond diagnosing, these models can pinpoint those who are at danger, allowing for early interventions and individualised treatment programmes. For effective application, collaboration with healthcare professionals, ethical considerations, and data privacy are essential.
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Schulze, B. and Angermeyer, M.C., 2003. Subjective experiences of stigma. A focus group study of schizophrenic patients, their relatives and mental health professionals. Social science & medicine, 56(2), pp.299-312.
Gorman, G.S., Chinnery, P.F., DiMauro, S., Hirano, M., Koga, Y., McFarland, R., Suomalainen, A., Thorburn, D.R., Zeviani, M. and Turnbull, D.M., 2016. Mitochondrial diseases. Nature reviews Disease primers, 2(1), pp.1-22.
Noor, M.B.T., Zenia, N.Z., Kaiser, M.S., Mamun, S.A. and 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, pp.1-21.
Bellack, Alan S. "Scientific and consumer models of recovery in schizophrenia: concordance, contrasts, and implications." (2006): 432-442.
Frith, C.D., 1979. Consciousness, information processing and schizophrenia. The British Journal of Psychiatry, 134(3), pp.225-235.
Leucht, S., Burkard, T., Henderson, J., Maj, M. and Sartorius, N., 2007. Physical illness and schizophrenia: a review of the literature. Acta Psychiatrica Scandinavica, 116(5), pp.317-333.
Wood, R. and Wand, A.P., 2014. The effectiveness of consultation-liaison psychiatry in the general hospital setting: a systematic review. Journal of psychosomatic research, 76(3), pp.175-192.
Spencer, T.J., Brown, A., Seidman, L.J., Valera, E.M., Makris, N., Lomedico, A., Faraone, S.V. and Biederman, J., 2013. Effect of psychostimulants on brain structure and function in ADHD: a qualitative literature review of magnetic resonance imaging-based neuroimaging studies. The Journal of clinical psychiatry, 74(9), p.5654.
Avberšek, L.K. and Repovš, G., 2022. Deep learning in neuroimaging data analysis: applications, challenges, and solutions. Frontiers in neuroimaging, 1, p.981642.
Eder, J., Dom, G., Gorwood, P., Kärkkäinen, H., Decraene, A., Kumpf, U., Beezhold, J., Samochowiec, J., Kurimay, T., Gaebel, W. and De Picker, L., 2023. Improving mental health care in depression: A call for action. European Psychiatry, 66(1), p.e65.
Vieira, S., Pinaya, W.H. and Mechelli, A., 2017. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience & Biobehavioral Reviews, 74, pp.58-75.
Kopelovich, S.L., Monroe-DeVita, M., Buck, B.E., Brenner, C., Moser, L., Jarskog, L.F., Harker, S. and Chwastiak, L.A., 2021. Community mental health care delivery during the COVID-19 pandemic: practical strategies for improving care for people with serious mental illness. Community mental health journal, 57, pp.405-415.
Amiri, Z., Heidari, A., Darbandi, M., Yazdani, Y., Jafari Navimipour, N., Esmaeilpour, M., Sheykhi, F. and Unal, M., 2023. The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors. Sustainability, 15(16), p.12406.
Hemalatha, D., Begum, A. and Hemanth, P., 2023, May. Early Detection of Breast Cancer with IoT: A Promising Solution. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-6). IEEE.
Devi, M.S., Kumar, A., Ravikumar, S., Yadav, R. and Singh, D., 2023, March. ThresholdedReLU Orthogonal Layer Weight Regularized Densely Connected Convolutional Networks CNN for Strawberry Disease Prediction. In 2023 2nd International Conference for Innovation in Technology (INOCON) (pp. 1-5). IEEE.
Y. Luo, Q. Tian, C. Wang, K. Zhang, C. Wang and J. Zhang, "Biomarkers for Prediction of Schizophrenia: Insights From Resting-State EEG Microstates," in IEEE Access, vol. 8, pp. 213078-213093, 2020, doi: 10.1109/ACCESS.2020.3037658.
Y. Zhu, S. Fu, S. Yang, P. Liang and Y. Tan, "Weighted Deep Forest for Schizophrenia Data Classification," in IEEE Access, vol. 8, pp. 62698-62705, 2020, doi: 10.1109/ACCESS.2020.2983317.
S, A.D., Begum, A. and Ravikumar, S. (2018) ‘Content clustering for MRI Image compression using PPAM’, International journal of engineering & technology [Preprint]. Available at: https://doi.org/10.14419/ijet.v7i1.7.10631.
Yassin, W., Nakatani, H., Zhu, Y., Kojima, M., Owada, K., Kuwabara, H., Gonoi, W., Aoki, Y., Takao, H., Natsubori, T. and Iwashiro, N., 2020. Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis. Translational psychiatry, 10(1), p.278.
Barros, C., Silva, C.A. and Pinheiro, A.P., 2021. Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls. Artificial intelligence in medicine, 114, p.102039.
Tønnesen, S., Kaufmann, T., de Lange, A.M.G., Richard, G., Doan, N.T., Alnæs, D., van der Meer, D., Rokicki, J., Moberget, T., Maximov, I.I. and Agartz, I., 2020. Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multisample diffusion tensor imaging study. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5(12), pp.1095-1103.
Cui, L.B., Cai, M., Wang, X.R., Zhu, Y.Q., Wang, L.X., Xi, Y.B., Wang, H.N., Zhu, X. and Yin, H., 2019. Prediction of early response to overall treatment for schizophrenia: a functional magnetic resonance imaging study. Brain and Behavior, 9(2), p.e01211.
Harvey, P.D., Strassnig, M.T. and Silberstein, J., 2019. Prediction of disability in schizophrenia: Symptoms, cognition, and self-assessment. Journal of Experimental Psychopathology, 10(3), p.2043808719865693.
Kalmady, S.V., Greiner, R., Agrawal, R., Shivakumar, V., Narayanaswamy, J.C., Brown, M.R., Greenshaw, A.J., Dursun, S.M. and Venkatasubramanian, G., 2019. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. npj Schizophrenia, 5(1), p.2.
Jakobsen, P., Garcia-Ceja, E., Stabell, L.A., Oedegaard, K.J., Berle, J.O., Thambawita, V., Hicks, S.A., Halvorsen, P., Fasmer, O.B. and Riegler, M.A., 2020, July. Psykose: A motor activity database of patients with schizophrenia. In 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) (pp. 303-308). IEEE.
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