Abstract
The reproducibility of Computer Aided Diagnosis (CAD) in detecting schizophrenia using neuroimaging modalities can provide early diagnosis of the disease. Schizophrenia is a psychiatric disorder that can lead to structural abnormalities in the brain, causing delusions and hallucinations. Neuroimaging modality such as a structural Magnetic Resonance Imaging (sMRI) technique can capture these structural abnormalities in the brain. Utilizing Machine Learning (ML) as a potential diagnostic tool in detecting classification biomarkers can aid clinical measures and cater to recognizing the factors underlying schizophrenia. This paper proposes an ML based model for the detection of schizophrenia on the structural MRI dataset of 146 subjects. We sought to classify schizophrenia and healthy control using five ML classifiers: Support Vector Machine, Logistic Regression, Decision Tree, k-Nearest Neighbor, and Random Forest. The raw structural MRI scans have been pre-processed using techniques such as image selection, image conversion, gray scaling of MRI images, and image flattening. Further, we have tested the performance of the model using hold-out cross-validation and stratified 10-fold cross-validation techniques. The results showed that the SVM achieved high accuracy when the dataset was validated using a stratified 10-fold cross-validation technique. On the other hand, k-Nearest Neighbor performed better when the hold-out validation method was used to evaluate the classifier.
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References
World health organization on schizophrenia. Schizophrenia. https://www.who.int/news-room/fact-sheets/detail/schizophrenia. Accessed 04 Oct 2022
Laursen, T.M., Nordentoft, M., Mortensen, P.B.: Excess early mortality in schizophrenia. Annu. Rev. Clin. Psychol. 10(1), 425–448 (2014)
Abinaya Sundari, R., Sujatha, C.M.: Identification of schizophrenia using LSTM recurrent neural network. In: 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), pp. 1–6 (2021)
Spitzer, R.L., Kroenke, K., Williams, J.B.W.: Diagnostic and statistical manual of mental disorders, 3rd edn. American Psychiatric Association (1980)
National Institute of Biomedical Imaging and Bioengineering (NIH). Magnetic resonance imaging (MRI). https://www.nibib.nih.gov/science-education/science-topics/magnetic-resonance-imaging-mri. Accessed 04 Oct 2022
Borgwardt, S., Andreou, C.: Structural and functional imaging markers for susceptibility to psychosis. Mol. Psychiatry 25, 2773–2785 (2020)
Tyagi, A., Singh, V.P., Gore, M.M.: Towards artificial intelligence in mental health: a comprehensive survey on the detection of schizophrenia. Multimedia Tools Appl. (2022)
Chang, C.-W., Ho, C.-C., Chen, J.-H.: ADHD classification by a texture analysis of anatomical brain MRI data. Front. Syst. Neurosci. 6, 66 (2012)
Sarraf, S., Tofighi, G.: Classification of Alzheimer’s disease using FMRI data and deep learning convolutional neural networks, March 2016
Xiao, Z., et al.: A deep learning-based segmentation method for brain tumor in MR images, pp. 1–6 (2016)
Zhu, Y., 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 Bull. 48(3), 563–574 (2022)
Schwarz, E., et al.: Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder. Transl. Psychiatry 9, 01 (2019)
Koshiyama, D., et al.: Neuroimaging studies within cognitive genetics collaborative research organization aiming to replicate and extend works of enigma. Hum. Brain Mapp. 43(1), 182–193 (2022)
Tanveer, M., Jangir, J., Ganaie, M.A., Beheshti, I., Tabish, M., Chhabra, N.: Diagnosis of schizophrenia: a comprehensive evaluation. IEEE J. Biomed. Health Inform. 1 (2022)
Chen, Z.H., et al.: Detecting abnormal brain regions in schizophrenia using structural MRI via machine learning. Comput. Intell. Neurosci. 13 (2020)
Guo, Y., Qiu, J., Lu, W.: Support vector machine-based schizophrenia classification using morphological information from amygdaloid and hippocampal subregions. Brain Sci. 10(8) (2020)
Winterburn, J.L., et al.: Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study. Schizophrenia Res. 214, 3–10 (2019)
Talpalaru, A., Bhagwat, N., Devenyi, G.A., Lepage, M., Chakravarty, M.M.: Identifying schizophrenia subgroups using clustering and supervised learning. Schizophrenia Res. 214, 51–59 (2019)
Skjerbæk, M.W., Foldager, J., Ambrosen, K.S., et al.: A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-Naïve schizophrenia patients based on multimodal neuropsychiatric data. Transl. Psychiatry 10(276) (2020)
IBM: Computer vision. https://www.ibm.com/in-en/topics/computer-vision. Accessed 27 Sept 2022
Stockman, G., Shapiro, L.G.: Computer Vision, 1st edn. Prentice Hall PTR, Hoboken (2001)
Liu, L., Wang, Y., Chi, W.: Image recognition technology based on machine learning. IEEE Access, 1 (2020)
Abbas Malik, M.G., Bashir, Z., Iqbal, N., Imtiaz, Md.A.: Color image encryption algorithm based on hyper-chaos and DNA computing. IEEE Access 8, 88093–88107 (2020)
Impact of image flattening. https://www.geeksforgeeks.org/impact-of-image-flattening/. Accessed 28 Sept 2022
Zhang, Y.: Support vector machine classification algorithm and its application. In: Liu, C., Wang, L., Yang, A. (eds.) Information Computing and Applications, pp. 179–186 (2012)
Gandhi, R.: Support vector machine - introduction to machine learning algorithms. In: Towards Data Science, 7 June 2018. Accessed 30 Sept 2022
Tyagi, A., Singh, V.P., Gore, M.M.: Improved detection of coronary artery disease using DT-RFE based feature selection and ensemble learning. In: Woungang, I., Dhurandher, S.K., Pattanaik, K.K., Verma, A., Verma, P. (eds.) ANTIC 2021. CCIS, vol. 1534, pp. 425–440. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-96040-7_34
Vetter, S.P., Regression, T.R.: The apple does not fall far from the tree. Anesthesia Analgesia 127(1), 277–283 (2018)
Vetter, T.R., Schober, P.: Linear regression in medical research. Anesthesia Analgesia 132, 108–109 (2021)
Vetter, T.R., Schober, P.: Logistic regression in medical research. Anesthesia Analgesia 132, 365–366 (2021)
Hastie, T., Tibshirani, R., Friedman, J.H., Friedman, J.H.: The Elements of Statistical Learning. SSS, Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7
Song, Y.-Y., Lu, Y.: Decision tree methods: applications for classification and prediction. Shanghai Arch. Psychiatry 27, 130–135 (2015)
Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: KNN model-based approach in classification. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) On the Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE, pp. 986–996 (2003)
Larose, D.T.: Discovering Knowledge in Data an Introduction to Data Mining, 2nd edn. Wiley, Hoboken (2005)
Breiman, L.: Classification and Regression Trees, 1st edn. Taylor and Francis Group, Boca Raton (1984)
Sarica, A., Cerasa, A., Quattrone, A.: Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: a systematic review. Front. Aging Neurosci. 9 (2017)
Refaeilzadeh, P., Tang, L., Liu, H.: Cross-Validation, pp. 532–538 (2009)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2, IJCAI 1995, pp. 1137–1143 (1995)
DataVedas: HOLDOUT CROSS-VALIDATION, 14 June 2018. Accessed 01 Oct 2022
Lyashenko, A.J.V.: Cross-Validation in Machine Learning: How to Do It Right. Neptune, 21 July 2022. Accessed 01 Oct 2022
The Mind Research Network for Neurodiagnostic Discovery. COBRE. https://www.mrn.org/common/cobre-phase-3. Accessed 25 Sept 2022
Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag. Process 5, 01–11 (2015)
Kulkarni, A., Chong, D., Batarseh, F.A.: 5 - foundations of data imbalance and solutions for a data democracy. In: Batarseh, F.A., Yang, R. (eds.) Data Democracy, pp. 83–106 (2020)
Wenxin, X.: Heart disease prediction model based on model ensemble. In: International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 195–199 (2020)
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Tyagi, A., Singh, V.P., Gore, M.M. (2023). Machine Learning Approaches for the Detection of Schizophrenia Using Structural MRI. In: Woungang, I., Dhurandher, S.K., Pattanaik, K.K., Verma, A., Verma, P. (eds) Advanced Network Technologies and Intelligent Computing. ANTIC 2022. Communications in Computer and Information Science, vol 1798. Springer, Cham. https://doi.org/10.1007/978-3-031-28183-9_30
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