Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers | Scientific Reports – Nature.com

DeFronzo, R. A. et al. Type 2 diabetes mellitus. Nat. Rev. Dis. Primers 2015(1), 122 (2015).

Google Scholar

Safiri, S. et al. Prevalence, deaths and disability-adjusted-life-years (DALYs) due to type 2 diabetes and its attributable risk factors in 204 countries and territories, 19902019: Results from the global Burden of disease study 2019. Front. Endocrinol. (Lausanne) 13, 114 (2022).

Article Google Scholar

Jelinek, H. F., Stranieri, A., Yatsko, A. & Venkatraman, S. Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis. Comput. Biol. Med. 75, 9097 (2016).

Article CAS PubMed Google Scholar

Mahat, R. K., Singh, N., Arora, M. & Rathore, V. Health risks and interventions in prediabetes: A review. Diabetes Metab. Syndr. Clin. Res. Rev. 13, 28032811 (2019).

Article Google Scholar

Yaribeygi, H., Sathyapalan, T., Atkin, S. L. & Sahebkar, A. Molecular Mechanisms Linking Oxidative Stress and Diabetes Mellitus. Oxid. Med. Cell Longev. 2020 (2020).

Sangwung, P., Petersen, K. F., Shulman, G. I. & Knowles, J. W. Potential role of alterations in mitochondrial function in the pathogenesis of insulin resistance and type 2 diabetes. Endocrinology (United States) 161, 110. https://doi.org/10.1210/ENDOCR/BQAA017 (2021).

Article Google Scholar

Oguntibeju, O. O. Type 2 diabetes mellitus, oxidative stress and inflammation: examining the links. Int. J. Physiol. Pathophysiol. Pharmacol. 11, 4563 (2019).

CAS PubMed PubMed Central Google Scholar

Voigt, A. & Jelinek, H. F. Humanin: A mitochondrial signaling peptide as a biomarker for impaired fasting glucose-related oxidative stress. Physiol. Rep. 4, 15 (2016).

Article Google Scholar

Jelinek, H. F., Helf, C. & Khalaf, K. Human SHC-transforming protein 1 and its isoforms p66shc: A novel marker for prediabetes. J. Diabetes Investig. https://doi.org/10.1111/JDI.13551 (2021).

Article PubMed PubMed Central Google Scholar

Maschirow, L., Khalaf, K., Al-Aubaidy, H. A. & Jelinek, H. F. Inflammation, coagulation, endothelial dysfunction and oxidative stress in prediabetes: Biomarkers as a possible tool for early disease detection for rural screening. Clin. Biochem. 48, 581585 (2015).

Article CAS PubMed Google Scholar

Jacob, S. M., Raimond, K. & Kanmani, D. Associated machine learning techniques based on diabetes based predictions, in 2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019 14451450 (2019) https://doi.org/10.1109/ICCS45141.2019.9065411.

Early Classification of Diabetes | Kaggle. https://www.kaggle.com/datasets/andrewmvd/early-diabetes-classification.

Paleczek, A., Grochala, D. & Rydosz, A. Artificial breath classification using xgboost algorithm for diabetes detection. Sensors 21, 745 (2021).

Article Google Scholar

Zeng, H. et al. Metabolic Biomarkers for Prognostic Prediction of Pre-diabetes: Results from a longitudinal cohort study. Sci. Rep. 7, 112 (2017).

Article Google Scholar

Lim, H., Kim, G. & Choi, J. H. Advancing diabetes prediction with a progressive self-transfer learning framework for discrete time series data. Sci. Rep. 13 (2023).

Fregoso-Aparicio, L., Noguez, J., Montesinos, L. & Garca-Garca, J. A. Machine learning and deep learning predictive models for type 2 diabetes: a systematic review. Diabetol Metab. Syndr. 13 (2021).

Wee, B. F., Sivakumar, S., Lim, K. H., Wong, W. K. & Juwono, F. H. Diabetes detection based on machine learning and deep learning approaches. Multimed. Tools Appl. https://doi.org/10.1007/s11042-023-16407-5 (2023).

Article Google Scholar

Oikonomou, E. K. & Khera, R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovasc. Diabetol. https://doi.org/10.1186/s12933-023-01985-3 (2023).

Article PubMed PubMed Central Google Scholar

Elshawi, R., Al-Mallah, M. H. & Sakr, S. On the interpretability of machine learning-based model for predicting hypertension. BMC Med. Inform. Decis. Mak. 19 (2019).

Khan, M. A. B. et al. Epidemiology of Type 2 diabetes: Global burden of disease and forecasted trends. J. Epidemiol. Glob Health 10, 107111 (2020).

Article PubMed PubMed Central Google Scholar

Sadeghi, S., Khalili, D., Ramezankhani, A., Mansournia, M. A. & Parsaeian, M. Diabetes mellitus risk prediction in the presence of class imbalance using flexible machine learning methods. BMC Med. Inform. Decis. Mak. 22, 113 (2022).

Article Google Scholar

Liu, L. et al. Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection. BMC Med. Inform. Decis. Mak. 22 (2022).

Elseddawy, A. I., Karim, F. K., Hussein, A. M. & Khafaga, D. S. Predictive analysis of diabetes-risk with class imbalance. Comput. Intell. Neurosci. 2022 (2022).

Rezvani, S. & Wang, X. A broad review on class imbalance learning techniques. Appl. Soft Comput. https://doi.org/10.1016/j.asoc.2023.110415 (2023).

Article Google Scholar

Barmparis, G. D., Marketou, M. E., Tsironis, G. P., Dritsas, E. & Trigka, M. Data-driven machine-learning methods for diabetes risk prediction. Sensors 22, 5304 (2022).

Article Google Scholar

Azad, C. et al. Prediction model using SMOTE, genetic algorithm and decision tree (PMSGD) for classification of diabetes mellitus. Multimed. Syst. 28, 12891307 (2022).

Article Google Scholar

Roy, K. et al. An enhanced machine learning framework for type 2 diabetes classification using imbalanced data with missing values. Complexity 2021 (2021).

Feng, X., Cai, Y. & Xin, R. Optimizing diabetes classification with a machine learning-based framework. BMC Bioinform. 24 (2023).

Tasin, I., Nabil, T. U., Islam, S. & Khan, R. Diabetes prediction using machine learning and explainable AI techniques. Healthc. Technol. Lett. 10, 110 (2023).

Article PubMed Google Scholar

Bellinger, C., Sharma, S. & Japkowicz, N. One-class versus binary classification: Which and when? in Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 vol. 2 102106 (2012).

Perera, P., Oza, P. & Patel, V. M. One-class classification: A survey. (2021).

Kang, S. Using binary classifiers for one-class classification. Expert Syst. Appl. 187 (2022).

Seliya, N., Abdollah Zadeh, A. & Khoshgoftaar, T. M. A Literature Review on One-Class Classification and Its Potential Applications in Big Data. Journal of Big Data vol. 8 (Springer, 2021).

Lpez-De-Ipia, K., Faundez-Zanuy, M., Sole, J., Zelarin, F. & Calvo, P. Multi-Class versus One-Class Classifier in Spontaneous Speech Analysis Oriented to Alzheimer Disease Diagnosis.

Vasighizaker, A., Sharma, A. & Dehzangi, A. A novel one-class classification approach to accurately predict disease-gene association in acute myeloid leukemia cancer. PLoS ONE 14 (2019).

Liu, X., Ouellette, S., Jamgochian, M., Liu, Y. & Rao, B. One-class machine learning classification of skin tissue based on manually scanned optical coherence tomography imaging. Sci. Rep. 13 (2023).

Argaw, P. N., Kushner, J. A., Kohane, I. S. & Paulson, H. J. A. Unsupervised Anomaly Detection to Characterize Heterogeneity in Type 2 Diabetes. in AMIA Jt Summits Transl Sci Proc 3241 (2023).

Fang, J. et al. Anomaly detection of diabetes data based on hierarchical clustering and CNN. in Procedia Computer Science vol. 199 7178 (Elsevier B.V., 2021).

Fitriyani, N. L. et al. Prediction Model for Type 2 Diabetes using Stacked Ensemble Classifiers. in 2020 International Conference on Decision Aid Sciences and Application, DASA 2020 399402 (Institute of Electrical and Electronics Engineers Inc., 2020). https://doi.org/10.1109/DASA51403.2020.9317090.

Dharmarathne, G., Jayasinghe, T. N., Bogahawaththa, M., Meddage, D. P. P. & Rathnayake, U. A novel machine learning approach for diagnosing diabetes with a self-explainable interface. Healthc. Analyt. 5 (2024).

Hendawi, R., Li, J. & Roy, S. A mobile app that addresses interpretability challenges in machine learningbased diabetes predictions: survey-based user study. JMIR Form Res 7 (2023).

Jakka, A. & Vakula Rani, J. An Explainable AI Approach for Diabetes Prediction. in Lecture Notes in Networks and Systems vol. 565 LNNS 1525 (Springer Science and Business Media Deutschland GmbH, 2023).

Jia, W. Standardising HbA1c-based diabetes diagnosis: Opportunities and challenges. Expert Rev. Mol. Diagn. 16, 343355 (2016).

Article CAS PubMed Google Scholar

Dorcely, B. et al. Novel biomarkers for prediabetes, diabetes, and associated complications. Diabetes Metab. Syndr. Obes. 10, 345361 (2017).

Article CAS PubMed PubMed Central Google Scholar

Wong, C. W. Vitamin B12 deficiency in the elderly: Is it worth screening?. Hong Kong Med. J. 21, 155164 (2015).

CAS PubMed Google Scholar

Australian Institute of Health and Welfare. Diabetes: Australian Facts, Summary. Diabetes https://www.aihw.gov.au/reports/diabetes/diabetes/contents/summary (2023).

Pouvreau, C., Dayre, A., Butkowski, E. G., De Jong, B. & Jelinek, H. F. Inflammation and oxidative stress markers in diabetes and hypertension. J. Inflamm. Res. 11, 6168 (2018).

Article CAS PubMed PubMed Central Google Scholar

Venkatraman, S., Yatsko, A., Stranieri, A. & Jelinek, H. F. Missing data imputation for individualised CVD diagnostic and treatment. Comput. Cardiol. 2010(43), 349352 (2016).

Google Scholar

Nwose, E. U., Jelinek, H. F., Richards, R. S. & Kerr, P. G. Changes in the erythrocyte glutathione concentration in the course of diabetes mellitus. Redox Rep. 11, 99104 (2006).

Article CAS PubMed Google Scholar

Zhao, J. et al. Triglyceride is an independent predictor of type 2 diabetes among middle-aged and older adults: A prospective study with 8-year follow-ups in two cohorts. J. Transl. Med. 17 (2019).

Abdul-Ghani, M. A. & DeFronzo, R. A. Plasma glucose concentration and prediction of future risk of type 2 diabetes. Diabetes care vol. 32 Suppl 2. https://doi.org/10.2337/dc09-s309 (2009).

Ganz, M. L. et al. The association of body mass index with the risk of type 2 diabetes: A case-control study nested in an electronic health records system in the United States. Diabetol. Metab. Syndr. 6 (2014).

Tony Liu, F., Ming Ting, K. & Zhou, Z.-H. Isolation forest ICDM08. ICDM (2008).

Carletti, M., Terzi, M. & Susto, G. A. Interpretable anomaly detection with DIFFI: Depth-based feature importance of isolation forest. Eng. Appl. Artif. Intell. 119 (2023).

Butkowski, E. G. & Jelinek, H. F. Hyperglycaemia, oxidative stress and inflammatory markers. Redox Rep. 22, 257264 (2017).

Article CAS PubMed Google Scholar

Lagman, M. et al. Investigating the causes for decreased levels of glutathione in individuals with type II diabetes. PLoS ONE 10, 119 (2015).

Article Google Scholar

Al-Goblan, A. S., Al-Alfi, M. A. & Khan, M. Z. Mechanism linking diabetes mellitus and obesity. Diabetes Metab. Syndr. Obes. 7, 587591 (2014).

Article PubMed PubMed Central Google Scholar

Leitner, D. R. et al. Obesity and type 2 diabetes: Two diseases with a need for combined treatment strategies - EASO can lead the way. Obes. Facts 10, 483492 (2017).

Article PubMed PubMed Central Google Scholar

Akbari, M. & Hassan-Zadeh, V. IL-6 signalling pathways and the development of type 2 diabetes. Inflammopharmacology 26, 685698. https://doi.org/10.1007/s10787-018-0458-0 (2018).

Article CAS PubMed Google Scholar

Ene, C. V., Nicolae, I., Geavlete, B., Geavlete, P. & Ene, C. D. IL-6 Signaling link between inflammatory tumor microenvironment and prostatic tumorigenesis. Analyt. Cell. Pathol. https://doi.org/10.1155/2022/5980387 (2022).

Article Google Scholar

Mirmira, R. G. et al. Interleukin-6 reduces B-cell oxidative stress by linking autophagy with the antioxidant response. in Diabetes vol. 67 15761588 (American Diabetes Association Inc., 2018).

Halimi, A. et al. The relation between serum levels of interleukin 10 and interferon-gamma with oral candidiasis in type 2 diabetes mellitus patients. BMC Endocr. Disord. 22 (2022).

Ayelign, B. et al. Association of IL-10 ( 1082 A/G) and IL-6 ( 174 G/C) gene polymorphism with type 2 diabetes mellitus in Ethiopia population. BMC Endocr. Disord. 21 (2021).

Abhilasha et al. Downregulation of interleukin-10 receptor (IL-10R) along with low serum IL-10 levels in newly diagnosed type 2 diabetes mellitus patients. Gene Rep. 24 (2021).

Carlini, V. et al. The multifaceted nature of IL-10: regulation, role in immunological homeostasis and its relevance to cancer, COVID-19 and post-COVID conditions. Front. Immunol. https://doi.org/10.3389/fimmu.2023.1161067 (2023).

Article PubMed PubMed Central Google Scholar

Jelinek, H., Jamil, D. & Al-Aubaidy, H. Impaired fasting glucose & 8-iso-prostaglandin F2 in diabetes disease progression. Br. J. Med. Med. Res. 4, 52295237 (2014).

Article Google Scholar

Schttker, B., Xuan, Y., Go, X., Anusruti, A. & Brenner, H. Oxidatively damaged DNA/RNA and 8-isoprostane levels are associated with the development of type 2 diabetes at older age: Results from a large cohort study. Diabetes Care 43, 130136 (2020).

Go here to read the rest:
Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers | Scientific Reports - Nature.com