A Hybrid Ensemble Based Binary Classifier for Early and Interpretable Detection of Genetic Disorders

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 6

Year of Publication : 2025

Authors : Ishdeep, Neetu Rani

10.14445/23488549/IJECE-V12I6P113

How to Cite?

Ishdeep, Neetu Rani, "A Hybrid Ensemble Based Binary Classifier for Early and Interpretable Detection of Genetic Disorders," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 6, pp. 158-183, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I6P113

Abstract:

Genetic disorders often stem from environmental or inherited DNA mutations, and early detection significantly improves life expectancy and reduces long-term healthcare costs for the commonwealth. Machine learning has proven effective in predicting and diagnosing such disorders, enabling treatment before the disorder hits a critical point. This research focuses on enhancing diagnostic accuracy using ensemble and bagging algorithms across three major genetic disorder groups while also making it economically inexpensive and less time-consuming by coining a hybrid ensemble-based binary classifier, the “Binary Multi-Model Disorder Classifier (BMMDC)”, a novel approach, which addresses limitations of the current standard multiclass classifiers, achieving an average of 95% accuracy over all disorders while also increasing the interpretability using explainable artificial intelligence.

Keywords:

Machine learning, BMMDC, Genetic Disorder, Gradient Boosting, LightGBM, XGBoost.

References:

[1] David W. Pfennig, Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies, Taylor & Francis, 2021.
[Google Scholar] [Publisher Link]
[2] Tiffany A. Kosch et al., “Genetic Approaches for Increasing Fitness in Endangered Species,” Trends in Ecology & Evolution, vol. 37, no. 4, pp. 332-345, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Mor Hanany, Carlo Rivolta, and Dror Sharon, “Worldwide Carrier Frequency and Genetic Prevalence of Autosomal Recessive Inherited Retinal Diseases,” Proceedings of the National Academy of Sciences, vol. 117, no. 5, pp. 2710-2716, 2020. [CrossRef] [Google Scholar] [Publisher Link]
[4] Nicholas Lench et al., “The Clinical Implementation of Non‐Invasive Prenatal Diagnosis for Single‐Gene Disorders: Challenges and Progress Made,” Prenatal Diagnosis, vol. 33, no. 6, pp. 555-562, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Dahlak Daniel Solomon et al., “Extensive Review on the Role of Machine Learning for Multifactorial Genetic Disorders Prediction,” Archives of Computational Methods in Engineering, vol. 31, pp. 623-640, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Gráinne S. Gorman et al., “Mitochondrial Diseases,” Nature Reviews Disease Primers, vol. 2, pp. 1-22, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Sameer Quazi, “Artificial Intelligence and Machine Learning in Precision and Genomic Medicine,” Medical Oncology, vol. 39, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Sreya Vadapalli et al., “Artificial Intelligence and Machine Learning Approaches using Gene Expression and Variant Data for Personalized Medicine,” Briefings in Bioinformatics, vol. 23, no. 5, pp. 1-25, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Ali Raza et al., “Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach,” Genes, vol. 14, no. 1, pp. 1-31, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Muhammad Umar Nasir et al., “Single and Mitochondrial Gene Inheritance Disorder Prediction using Machine Learning,” Computers, Materials & Continua, vol. 73, no. 1, pp. 953-963, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Zodwa Dlamini et al., “Artificial Intelligence (AI) and Big Data in Cancer and Precision Oncology,” Computational and Structural Biotechnology Journal, vol. 18, pp. 2300-2311, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Catriona Miller et al., “A Review of Model Evaluation Metrics for Machine Learning in Genetics and Genomics,” Frontiers in Bioinformatics, vol. 4, pp. 1-13, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Sofia Singh et al., “Enhancing Genomic Disorder Prediction through Feynman Concordance and Interpolated Nearest Centroid Techniques,” Scientific Reports, vol. 14, pp. 1-21, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] C.R. Haldeman-Englert et al., GeneReviews®, Europepmc, 1993.
[Google Scholar] [Publisher Link]
[15] Shuo Yang et al., “Long-Term Outcomes of Gene Therapy for the Treatment of Leber's Hereditary Optic Neuropathy,” EBioMedicine, vol. 10, pp. 258-268, 2016.
[Google Scholar] [Publisher Link]
[16] Shibani Kanung et al., “Mitochondrial Disorders,” Annals of Translational Medicine, vol. 6, no. 24, pp. 1-27, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Svetlana Yovinska et al., “e-Posters EP01 Reproductive Genetics,” European Journal of Human Genetics, vol. 31, pp. 91-344, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Bryce A. Pasqualotto et al., “Galactose-Replacement Unmasks the Biochemical Consequences of the G11778A Mitochondrial DNA Mutation of LHON in Patient-Derived Fibroblasts,” Experimental Cell Research, vol. 439, no. 1, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Mohammed Hasan Barrak et al., “Pathophysiology, the Biochemical and Clinical Significance of Lactate Dehydrogenase,” International Journal of Health and Medical Research, vol. 3, no. 7, pp. 440-443, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Jia-Der Ju Wang et al., “Sleep and Breathing Disturbances in Children with Leigh Syndrome: A Comparative Study,” Pediatric Neurology, vol. 136, pp. 56-63, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Inn-Chi Lee, and Kuo-Liang Chiang, “Clinical Diagnosis and Treatment of Leigh Syndrome Based on SURF1: Genotype and Phenotype,” Antioxidants, vol. 10, no. 12, pp. 1-14, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Albert Z. Lim et al., “Natural History of Leigh Syndrome: A Study of Disease Burden and Progression,” Annals of Neurology, vol. 91, no. 1, pp. 117-130, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Klervie Loiselet et al., “Cerebral Blood Flow and Acute Episodes of Leigh Syndrome in Neurometabolic Disorders,” Developmental Medicine & Child Neurology, vol. 63, no. 6, pp. 705-711, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Grayson Beecher, Mark D. Fleming, and Teerin Liewluck, “Hereditary Myopathies Associated with Hematological Abnormalities,” Muscle & Nerve, vol. 65, no. 4, pp. 374-390, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Gina Perez Giraldo et al., “Neuromyelitis Optica Spectrum Disorder With Comorbid Complex 1 Mitochondrial Disease, Leukopenia And Neutropenia, A Challenging Case With Difficult Management (P7-1.005),” Neurology, vol. 98, no. 18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Tina D. Jeppesen et al., “Exercise Testing, Physical Training and Fatigue in Patients with Mitochondrial Myopathy Related to mtDNA Mutations,” Journal of Clinical Medicine, vol. 10, no. 8, pp. 1-19, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Kavya Bharathidasan et al., “Mitochondrial Myopathy in a 21-Year-Old Man Presenting with Bilateral Lower Extremity Weakness and Swelling,” Journal of Primary Care & Community Health, vol. 14, pp. 1-8, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Puneet K. Samaiya, Sairam Krishnamurthy, and Ashok Kumar, “Mitochondrial Dysfunction in Perinatal Asphyxia: Role in Pathogenesis and Potential Therapeutic Interventions,” Molecular and Cellular Biochemistry, vol. 476, no. 12, pp. 4421-4434, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Vincenzo Tragni et al., “Personalized Medicine in Mitochondrial Health and Disease: Molecular Basis of Therapeutic Approaches based on Nutritional Supplements and Their Analogs,” Molecules, vol. 27, no. 11, pp. 1-49, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Yi Shiau Ng et al., “Mitochondrial Disease in Adults: Recent Advances and Future Promise,” The Lancet Neurology, vol. 20, no. 7, pp. 573-584, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Po-Yu Lin et al., “Exacerbation of Myopathy Triggered by Antiobesity Drugs in a Patient with Multiple Acyl-CoA Dehydrogenase Deficiency,” BMC Neurology, vol. 21, pp. 1-5, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Raziye Melike Yildirim, and Emre Seli “Mitochondria as Therapeutic Targets in Assisted Reproduction,” Human Reproduction, vol. 39, no. 10, pp. 2147-2159, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Alessia Adelizzi et al., “Fetal and Obstetrics Manifestations of Mitochondrial Diseases,” Journal of Translational Medicine, vol. 22, pp. 1-30, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Rajeev Bhatia, Bruce H. Cohen, and Neil L. McNinch, “A Novel Exercise Testing Algorithm to Diagnose Mitochondrial Myopathy,” Muscle & Nerve, vol. 63, no. 5, pp. 715-723, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Yaqi Wang et al., “The Relationship between Erythrocytes and Diabetes Mellitus,” Journal of Diabetes Research, vol. 2021, no. 1, pp. 1-9, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Atsuhiko Kawabe et al., “WBC Count Predicts Heart Failure in Diabetes and Coronary Artery Disease Patients: A Retrospective Cohort Study,” ESC Heart Failure, vol. 8, no. 5, pp. 3748-3759, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Giovanni Corona et al., “Diabetes is Most Important Cause for Mortality in COVID-19 Hospitalized Patients: Systematic Review and Meta-Analysis,” Reviews in Endocrine and Metabolic Disorders, vol. 22, pp. 275-296, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Antonino Tuttolomondo et al., “Assessment of Heart Rate Variability (HRV) in Subjects with Type 2 Diabetes Mellitus with and Without Diabetic Foot: Correlations with Endothelial Dysfunction Indices and Markers of Adipo-Inflammatory Dysfunction,” Cardiovascular Diabetology, vol. 20, pp. 1-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Chun-Hua Liu et al., “Effect of Birth Asphyxia on Neonatal Blood Glucose during the Early Postnatal Life: A Multi-Center Study in Hubei Province, China,” Pediatrics & Neonatology, vol. 64, no. 5, pp. 562-569, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Omid Asbaghi et al., “Folic Acid Supplementation Improves Glycemic Control for Diabetes Prevention and Management: A Systematic Review and Dose-Response Meta-Analysis of Randomized Controlled Trials,” Nutrients, vol. 13, no. 7, pp. 1-25, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Asher Ornoy et al., “Diabetes During Pregnancy: A Maternal Disease Complicating the Course of Pregnancy with Long-Term Deleterious Effects on the Offspring. A Clinical Review,” International Journal of Molecular Sciences, vol. 22, no. 6, pp. 1-36, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Jagdish Gopal Paithankar, Subash Chandra Gupta, and Anurag Sharma, “Therapeutic Potential of Low Dose Ionizing Radiation against Cancer, Dementia, and Diabetes: Evidences from Epidemiological, Clinical, and Preclinical Studies,” Molecular Biology Reports, vol. 50, pp. 2823-2834, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[43] Siyu Zhu et al., “Adverse Childhood Experiences and Risk of Diabetes: A Systematic Review and Meta-Analysis,” Journal of Global Health, vol. 12, pp. 1-11, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Yehia Moustafa Ghanem et al., “Potential Risk of Gestational Diabetes Mellitus in Females Undergoing in Vitro Fertilization: A Pilot Study,” Clinical Diabetes and Endocrinology, vol. 10, no. 1, pp. 1-6, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Linn Håkonsen Arendt et al., “Glycemic Control in Pregnancies Complicated by Pre-Existing Diabetes Mellitus and Congenital Malformations: A Danish Population-Based Study,” Clinical Epidemiology, pp. 615-626, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[46] Matias Vaajala et al., “Previous Induced Abortion or Miscarriage is Associated with Increased Odds for Gestational Diabetes: A Nationwide Register-Based Cohort Study in Finland,” Acta Diabetologica, vol. 60, pp. 845-849, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[47] Stephanie L. Marchincin et al., “Risk of Birth Defects by Pregestational Type 1 or Type 2 Diabetes: National Birth Defects Prevention Study, 1997–2011,” Birth Defects Research, vol. 115, no. 1, pp. 56-66, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[48] Fausto Petrelli et al., “Red Blood Cell Transfusions and the Survival in Patients with Cancer Undergoing Curative Surgery: A Systematic Review and Meta-Analysis,” Surgery Today, vol. 51, pp. 1535-1557, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[49] Pradeep S. Virdee et al., “The Association between Blood Test Trends and Undiagnosed Cancer: A Systematic Review and Critical Appraisal,” Cancers, vol. 16, no. 9, pp. 1-13, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[50] Sakiko Fukui et al., “Association between Respiratory and Heart Rate Fluctuations and Death Occurrence in Dying Cancer Patients: Continuous Measurement with a Non-Wearable Monitor,” Supportive Care in Cancer, vol. 30, pp. 77-86, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[51] Yu-Jie Su et al., “Prevalence and Risk Factors Associated with Birth Asphyxia Among Neonates Delivered in China: A Systematic Review and Meta-Analysis,” BMC Pediatrics, vol. 24, pp. 1-16, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[52] Haidara Kherbek et al., “The Relationship between Folic Acid and Colorectal Cancer; A Literature Review,” Annals of Medicine and Surgery, vol. 80, pp. 1-4, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[53] Audrey Bonaventure et al., “Maternal Illnesses during Pregnancy and the Risk of Childhood Cancer: A Medical‐Record based Analysis (UKCCS),” International Journal of Cancer, vol. 156, no. 5, pp. 920-929, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[54] Sheikh Ahmad Umar, and Sheikh Abdullah Tasduq, “Ozone Layer Depletion and Emerging Public Health Concerns-An Update on Epidemiological Perspective of the Ambivalent Effects of Ultraviolet Radiation Exposure,” Frontiers in Oncology, vol. 12, pp. 1-11, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[55] Xu Ji et al., “Substance Use, Substance Use Disorders, and Treatment in Adolescent and Young Adult Cancer Survivors—Results from a National Survey,” Cancer, vol. 127, no. 17, pp. 3223-3231, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[56] M. Condorelli et al., “Impact of ARTs on Oncological Outcomes in Young Breast Cancer Survivors,” Human Reproduction, vol. 36, no. 2, pp. 381-389, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[57] Shinje Moon, Ka Hee Yi, and Young Joo Park, “Risk of Adverse Pregnancy Outcomes in Young Women with Thyroid Cancer: A Systematic Review and Meta-Analysis,” Cancers, vol. 14, no. 10, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[58] Anders Husby, Jan Wohlfahrt, and Mads Melbye, “Pregnancy Duration and Ovarian Cancer Risk: A 50‐Year Nationwide Cohort Study,” International Journal of Cancer, vol. 151, no. 10, pp. 1717-1725, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[59] Jessica S.W Borgers et al., “Immunotherapy for Cancer Treatment during Pregnancy,” The Lancet Oncology, vol. 22, no. 12, pp. 550-561, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[60] Le-Tian Huang et al., “Association of Peripheral Blood Cell Profile with Alzheimer's Disease: A Meta-Analysis,” Frontiers in Aging Neuroscience, vol. 14, pp. 1-14, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[61] Usma Munawara et al., “Hyperactivation of Monocytes and Macrophages in MCI Patients Contributes to the Progression of Alzheimer's Disease,” Immunity & Ageing, vol. 18, pp. 1-25, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[62] Showkat Ul Nabi et al., “Mechanisms of Mitochondrial Malfunction in Alzheimer’s Disease: New Therapeutic Hope,” Oxidative Medicine and Cellular Longevity, vol. 2022, no. 1, pp. 1-28, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[63] Yume Imahori et al., “Association of Resting Heart Rate with Cognitive Decline and Dementia in Older Adults: A Population‐Based Cohort Study,” Alzheimer's & Dementia, vol. 18, no. 10, pp. 1779-1787, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[64] Agata Tarkowska et al., “Preservation of Biomarkers Associated with Alzheimer’s Disease (Amyloid Peptides 1-38, 1-40, 1-42, Tau Protein, Beclin 1) in the Blood of Neonates after Perinatal Asphyxia,” International Journal of Molecular Sciences, vol. 24, no. 17, pp. 1-11, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[65] Cian Carey et al., “Hypertensive Disorders of Pregnancy and the Risk of Maternal Dementia: A Systematic Review and Meta-Analysis,” American Journal of Obstetrics and Gynecology, vol. 231, no. 2, pp. 1-15, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[66] Kathleen B. Miller et al., “Ionizing Radiation, Cerebrovascular Disease, and Consequent Dementia: A Review and Proposed Framework Relevant to Space Radiation Exposure,” Frontiers in Physiology, vol. 13, pp. 1-26, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[67] María P. Aranda et al., “The Relationship of History of Psychiatric and Substance Use Disorders on Risk of Dementia among Racial and Ethnic Groups in the United States,” Frontiers in Psychiatry, vol. 14, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[68] Magdalena Pszczołowska et al., “Association between Female Reproductive Factors and Risk of Dementia,” Journal of Clinical Medicine, vol. 13, no. 10, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[69] Laia Montoliu-Gaya et al., “Blood Biomarkers for Alzheimer’s Disease in Down Syndrome,” Journal of Clinical Medicine, vol. 10, no. 16, pp. 1-21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[70] Theodoros Karampitsakos et al., “Increased Monocyte Count and Red Cell Distribution Width as Prognostic Biomarkers in Patients with Idiopathic Pulmonary Fibrosis,” Respiratory Research, vol. 22, pp. 1-10, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[71] Monica Averna, Paola Melotti, and Claudio Sorio, “Revisiting the Role of Leukocytes in Cystic Fibrosis,” Cells, vol. 10, no. 12, pp. 1-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[72] Gabriela Oates et al., “The Association of Area Deprivation and State Child Health with Respiratory Outcomes of Pediatric Patients with Cystic Fibrosis in the United States,” Pediatric Pulmonology, vol. 56, no. 5, pp. 883-890, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[73] T. Spencer Poore, Jennifer L. Taylor-Cousar, and Edith T. Zemanick, “Cardiovascular Complications in Cystic Fibrosis: A Review of the Literature,” Journal of Cystic Fibrosis, vol. 21, no. 1, pp. 18-25, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[74] Sanaz Vaziri et al., “Time to be Blunt: Substance Use in Cystic Fibrosis,” Pediatric Pulmonology, vol. 59, no. 4, pp. 1015-1027, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[75] Mabel Aoun, Michel Jadoul, and Hans-Joachim Anders, “Erythrocytosis and CKD: A Review,” American Journal of Kidney Diseases, vol. 84, no. 4, pp. 495-506, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[76] Roshini Kurian, Preethu Anand, and George Ghaly, “A Late and Complex Presentation of Hereditary Haemochromatosis,” Cureus, vol. 14, no. 11, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[77] Jinling Wang et al., “Case Report: A Rare Case of Hereditary Hemochromatosis Caused by a Mutation in the HAMP Gene in Fuyang, China,” Frontiers in Medicine, vol. 11, pp. 1-6, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[78] Michał Świątczak et al., “The Potential Impact of Hereditary Hemochromatosis on the Heart Considering the Disease Stage and Patient Age—The Role of Echocardiography,” Frontiers in Cardiovascular Medicine, vol. 10, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[79] Danielle S. Kroll et al., “Elevated Transferrin Saturation in Individuals with Alcohol Use Disorder: Association with HFE Polymorphism and Alcohol Withdrawal Severity,” Addiction Biology, vol. 27, no. 2, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[80] Ambrin Gull Shamas, “Primary Hereditary Haemochromatosis and Pregnancy,” GastroHep, vol. 2023, no. 1, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[81] Corneliu Toader et al., “From Recognition to Remedy: The Significance of Biomarkers in Neurodegenerative Disease Pathology,” International Journal of Molecular Sciences, vol. 24, no. 22, pp. 1-32, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[82] Thomas Kluyver et al., Jupyter Notebooks—A Publishing Format for Reproducible Computational Workflows, Positioning and Power in Academic Publishing: Players, Agents, and Agendas, IOS Press, pp. 87-90, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[83] Visual Studio Code Documentation, Microsoft, 2023. [Online]. Available: https://code.visualstudio.com/docs 
[84] Python 3.11.0 Release Notes, Python Software Foundation, 2024. [Online]. Available: https://www.python.org/downloads/release/python-3110/
[85] Wes McKinney, “Data Structures for Statistical Computing in Python,” Proceedings of the 9th Python in Science Conference, Austin, TX, pp. 56-61, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[86] Charles R. Harris et al., “Array Programming with NumPy,” Nature, vol. 585, pp. 357-362, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[87] Fabian Pedregosa et al., “Scikit-Learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
[Google Scholar] [Publisher Link]
[88] John D. Hunter, “Matplotlib: A 2D Graphics Environment,” Computing in Science & Engineering, vol. 9, pp. 90-95, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[89] Michael L. Waskom, “Seaborn: Statistical Data Visualization,” Journal of Open Source Software, vol. 6, no. 60, pp. 1-4, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[90] Tianqi Chen, and Carlos Ernesto Guestrin, “XGBoost: A Scalable Tree Boosting System,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, pp. 785-794, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[91] Guillaume Lemaître, Fernando Nogueira, and Christos K. Aridas, “Imbalanced-Learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning,” Journal of Machine Learning Research, vol. 18, no. 17, pp. 1-5, 2017.
[Google Scholar] [Publisher Link]
[92] Cencheng Shen, Sambit Panda, and Joshua T. Vogelstein, “The Chi-Square Test of Distance Correlation,” Journal of Computational and Graphical Statistics, vol. 31, no. 1, pp. 254-262, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[93] P. Yu-Wai-Man et al., “Inherited Mitochondrial Optic Neuropathies,” Journal of Medical Genetics, vol. 46, pp. 148-158, 2009.
[CrossRef] [Google Scholar] [Publisher Link]