Machine learning approach for bipolar disorder analysis and recognition based on handwriting digital images

[1] F. Anowar, S. Sadaoui, and B. Selim, Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE), Computer Science Review, 40 (2021), p. 100378.

[2] L. Ayerbe, I. Forgnone, J. Addo, A. Siguero, S. Gelati, and S. Ayis, Hypertension risk and clinical care in patients with bipolar disorder or schizophrenia; a systematic review and meta-analysis, Journal of affective disorders, 225 (2018), pp. 665–670.

[3] C. C. Bennett, M. K. Ross, E. Baek, D. Kim, and A. D. Leow, Predicting clinically relevant changes in bipolar disorder outside the clinic walls based on pervasive technology interactions via smartphone typing dynamics, Pervasive and Mobile Computing, 83 (2022), p. 101598.

[4] B. Birmaher, Bipolar disorder in children and adolescents, Child and adolescent mental health, 18 (2013), pp. 140–148.

[5] K. Chaudhari and A. Thakkar, Survey on handwriting-based personality trait identification, Expert sys[1]tems with applications, 124 (2019), pp. 282–308.

[6] Y. Chernov and C. Caspers, Formalized computer-aided handwriting psychology: Validation and integration into psychological assessment, Behavioral Sciences, 10 (2020), p. 27.

[7] F. Colombo, F. Calesella, M. G. Mazza, E. M. T. Melloni, M. J. Morelli, G. M. Scotti, F. Benedetti, I. Bollettini, and B. Vai, Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: A systematic review and meta-analysis, Neuro[1]science & Biobehavioral Reviews, 135 (2022), p. 104552.

[8] F. De Crescenzo, G. Serra, F. Maisto, M. Uchida, H. Woodworth, M. P. Casini, R. J. Baldessarini, and S. Vicari, Suicide attempts in juvenile bipolar versus major depressive disorders: systematic review and meta-analysis, Journal of the American Academy of Child & Adolescent Psychiatry, 56 (2017), pp. 825–831.

[9] J. de Prado-Gil, C. Palencia, P. Jagadesh, and R. Mart´ınez-Garc´ıa, A comparison of machine learning tools that model the splitting tensile strength of self-compacting recycled aggregate concrete, Materials, 15 (2022), p. 4164.

[10] P. Dome, Z. Rihmer, and X. Gonda, Suicide risk in bipolar disorder: a brief review, Medicina, 55 (2019), p. 403.

[11] M. Faurholt-Jepsen, E. M. Christensen, M. Frost, J. E. Bardram, M. Vinberg, and L. V. Kessing, Hypomania/mania by dsm-5 definition based on daily smartphone-based patient-reported assessments, Journal of affective disorders, 264 (2020), pp. 272–278.

[12] J. Gao, B. Wang, Z. Wang, Y. Wang, and F. Kong, A wavelet transform-based image segmentation method, Optik, 208 (2020), p. 164123.

[13] B. Geller and J. Luby, Child and adolescent bipolar disorder: a review of the past 10 years, Journal of the American Academy of Child & Adolescent Psychiatry, 36 (1997), pp. 1168–76.

[14] S. Ghosh, Comparative analysis of boosting algorithms over mnist handwritten digit dataset, in Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2021, Springer, 2022, pp. 985–995.

[15] F. J. A. Gordovez and F. J. McMahon, The genetics of bipolar disorder, Molecular psychiatry, 25 (2020), pp. 544–559.

[16] B. S. Graham and C. C. de Xavier Pinto, Semiparametrically efficient estimation of the average linear regression function, Journal of Econometrics, 226 (2022), pp. 115–138.

[17] T. K. Gupta and K. Raza, Optimization of ANN architecture: a review on nature-inspired techniques, Machine learning in bio-signal analysis and diagnostic imaging, (2019), pp. 159–182.

[18] Y. Huang, L. Li, Y. Gan, C. Wang, H. Jiang, S. Cao, and Z. Lu, Sedentary behaviors and risk of depression: a meta-analysis of prospective studies, Translational psychiatry, 10 (2020), p. 26.

[19] A. F. Jahwar and A. M. Abdulazeez, Meta-heuristic algorithms for K-means clustering: A review, PalArch’s Journal of Archaeology of Egypt/Egyptology, 17 (2020), pp. 12002–12020.

[20] A. Jamali, Bipolar vs non bipolar handwriting. https://www.kaggle.com/datasets/ahmadalijamali/ bipolar-vs-non-bipolar-handwriting. Accessed: 2023.

[21] , Data analysis and modelling bipolar vs non bipolar. https://www.kaggle.com/code/ahmadalijamali/ data-analysis-and-modelling-bipolar-vs-non-bipolar. Accessed: 2023.

[22] A. Jamali, M. Rostamy-Malkhalifeh, and R. Kargar, The novel self-organizing map combined with fuzzy C-means and K-means convolution for a soft and hard natural digital image segmentation, AUT Journal of Mathematics and Computing, 5 (2024), pp. 151–165.

[23] C. A. Kieslich, F. Alimirzaei, H. Song, M. Do, and P. Hall, Data-driven prediction of antiviral peptides based on periodicities of amino acid properties, in Computer Aided Chemical Engineering, vol. 50, Elsevier, 2021, pp. 2019–2024.

[24] M. Kopec, G. Garbacz, A. Brodecki, and Z. L. Kowalewski, Metric entropy and digital image correlation in deformation dynamics analysis of fibre glass reinforced composite under uniaxial tension, Measurement, 205 (2022), p. 112196.

[25] A. McGuinness, J. A. Davis, S. Dawson, A. Loughman, F. Collier, M. O’hely, C. Simpson, J. Green, W. Marx, C. Hair, et al., A systematic review of gut microbiota composition in observa[1]tional studies of major depressive disorder, bipolar disorder and schizophrenia, Molecular psychiatry, 27 (2022), pp. 1920–1935.

[26] T. Messer, G. Lammers, F. Muller-Siecheneder, R.-F. Schmidt, and S. Latifi ¨ , Substance abuse in patients with bipolar disorder: A systematic review and meta-analysis, Psychiatry research, 253 (2017), pp. 338–350.

[27] B. Mwangi, M.-J. Wu, B. Cao, I. C. Passos, L. Lavagnino, Z. Keser, G. B. Zunta-Soares, K. M. Hasan, F. Kapczinski, and J. C. Soares, Individualized prediction and clinical staging of bipolar disorders using neuroanatomical biomarkers, Biological psychiatry: cognitive neuroscience and neuroimaging, 1 (2016), pp. 186–194.

[28] J. A. Nolazco-Flores, M. Faundez-Zanuy, O. A. Velazquez-Flores, C. Del-Valle-Soto, G. Cor- ´ dasco, and A. Esposito, Mood state detection in handwritten tasks using PCA–mFCBF and automated machine learning, Sensors, 22 (2022), p. 1686.

[29] A. Nusret, O. Celbis, E. P. Zayman, R. Karlidag, and B. S. ˘ Onar ¨ , The use of handwriting changes for the follow-up of patients with bipolar disorder, Archives of Neuropsychiatry, 59 (2022), p. 3.

[30] G. Olivito, M. Lupo, A. Gragnani, M. Saettoni, L. Siciliano, C. Pancheri, M. Panfili, M. Cercignani, M. Bozzali, R. D. Chiaie, et al., Aberrant cerebello-cerebral connectivity in remitted bipolar patients 1 and 2: new insight into understanding the cerebellar role in mania and hypomania, The Cerebellum, 21 (2022), pp. 647–656.

[31] S. Pare, A. Kumar, G. K. Singh, and V. Bajaj, Image segmentation using multilevel thresholding: a research review, Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 44 (2020), pp. 1–29.

[32] I. Perez Arribas, G. M. Goodwin, J. R. Geddes, T. Lyons, and K. E. Saunders, A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder, Translational psychiatry, 8 (2018), p. 274.

[33] M. Rajabi, H. Golshan, and R. P. Hasanzadeh, Non-local adaptive hysteresis despeckling approach for medical ultrasound images, Biomedical Signal Processing and Control, 85 (2023), p. 105042.

[34] M. Rajabi and R. P. Hasanzadeh, A modified adaptive hysteresis smoothing approach for image denoising based on spatial domain redundancy, Sensing and Imaging, 22 (2021), p. 42.

[35] G. Ryan, P. Katarina, and D. Suhartono, Mbti personality prediction using machine learning and smote for balancing data based on statement sentences, Information, 14 (2023), p. 217.

[36] M. K. Singh, R. G. Kelley, K. D. Chang, and I. H. Gotlib, Intrinsic amygdala functional connectivity in youth with bipolar I disorder, Journal of the American Academy of Child & Adolescent Psychiatry, 54 (2015), pp. 763–770.

[37] T. Smith Jr, W. Marks, G. Lange, W. Sheriff Jr, and E. Neale, Edge detection in images using Marr-Hildreth filtering techniques, Journal of neuroscience methods, 26 (1988), pp. 75–81.

[38] J. L. Speiser, M. E. Miller, J. Tooze, and E. Ip, A comparison of random forest variable selection methods for classification prediction modeling, Expert systems with applications, 134 (2019), pp. 93–101.

[39] P. Steullet, J.-H. Cabungcal, S. A. Bukhari, M. I. Ardelt, H. Pantazopoulos, F. Hamati, T. E. Salt, M. Cuenod, K. Q. Do, and S. Berretta, The thalamic reticular nucleus in schizophrenia and bipolar disorder: role of parvalbumin-expressing neuron networks and oxidative stress, Molecular psychiatry, 23 (2018), pp. 2057–2065.

[40] R. Thillaikkarasi and S. Saravanan, An enhancement of deep learning algorithm for brain tumor segmentation using kernel based CNN with M-SVM, Journal of medical systems, 43 (2019), p. 84.

[41] H. Tyralis, G. Papacharalampous, and A. Langousis, A brief review of random forests for water scientists and practitioners and their recent history in water resources, Water, 11 (2019), p. 910.

[42] E. H. Umeoka, J. M. van Leeuwen, C. H. Vinkers, and M. Joels ¨ , The role of stress in bipolar disorder, Bipolar Disorder: From Neuroscience to Treatment, (2021), pp. 21–39.

[43] E. Vieta, M. Berk, T. G. Schulze, A. F. Carvalho, T. Suppes, J. R. Calabrese, K. Gao, K. W. Miskowiak, and I. Grande, Bipolar disorders, Nature reviews Disease primers, 4 (2018), pp. 1–16.

[44] G. Wang, C. Lopez-Molina, and B. De Baets, Automated blob detection using iterative laplacian of Gaussian filtering and unilateral second-order Gaussian kernels, Digital Signal Processing, 96 (2020), p. 102592.

[45] C. Yuan and H. Yang, Research on K-value selection method of K-means clustering algorithm, J, 2 (2019), pp. 226–235.

[46] T. Zhou, S. Ruan, and S. Canu, A review: Deep learning for medical image segmentation using multimodality fusion, Array, 3 (2019), p. 100004.