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Assessment of the risk factors of type II diabetes using ACO with self-regulative update function and decision trees by evaluation from Fisher’s Z-transformation

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Abstract

Type II diabetes is considered to be one of the persistent diseases which is the cause of death and disability in many regions. The objective of this research work is to apply an improved combination of the ant colony optimization (ACO) algorithm with decision trees (J48) for accessing and evaluating the risk factors related to type II diabetes. The model developed concerning the routine and deviated values for each attribute corresponding to type II diabetes. Experimental evaluation has been made with the modified self-regulative function of ACO with enhancement in pheromone update rule to make the ants in the search space converge at the best optimal path. In addition to this, continuous assessment has been made with probabilistic function by the construction of new solution incrementally made by the ants through feature by feature analysis. From the results, it has been observed that the risk factors corresponding to type II diabetes are postprandial plasma glucose (PPG), fasting plasma glucose (FPG), and glycosylated hemoglobin (A1c) which has selected with an improved accuracy than that of the existing methods and algorithms. The efficiency of prediction has been tested using Fisher’s Z-transformation with a 95% of confidence level for upper and lower bounds. From the inference, it has been observed that there exists a strong correlation among the risk factors PPF and FPG with significance in P-value for the risk corresponding to type II diabetes. Hence, predictive analytics with improvement in ACO with C4.5 decision tree algorithm can also be deployed for accessing the risk factors related to NCD such as cancer, heart disease, and kidney diseases.

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References

  1. Weiss SM, Kulikowski CA, Amarel S, Safir A (1978) A model-based method for computer-aided medical decision making. Artif Intellig 11(1):145–172

    Article  Google Scholar 

  2. Collen MF (1994) The origins of informatics. J Am Medic Inform Assoc 1(2):91–107

    Article  CAS  Google Scholar 

  3. Sheik Abdullah A, Manoj A, Selvakumar S (2021) Assessment and Evaluation of cancer CT images using deep learning Techniques. 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC) [Internet]. IEEE; 2021 May 21; Available from: https://doi.org/10.1109/icsccc51823.2021.9478176

  4. Sheik Abdullah A, Selvakumar S, Parkavi R, Suganya R, Venkatesh M (2019) An introduction to survival analytics, types, and its applications. Biomechanics. https://doi.org/10.5772/intechopen.80953

    Article  Google Scholar 

  5. Babad H, Sanderson C, Naidoo B, White I, Wang D (2002) The development of a simulation model of primary prevention strategies for coronary heart disease. Health Care Manag Sci 5:269–274

    Article  PubMed  Google Scholar 

  6. Sheik Abdullah A, Manoj A, Tarun Kishore GT, Selvakumar S (2021) A new approach to remote health monitoring using augmented reality with WebRTC and WebXR. 22nd International Arab Conference on Information Technology (ACIT) [Internet]. IEEE; 2021 Dec 21; Available from: https://doi.org/10.1109/acit53391.2021.9677324

  7. Jesmin N, Imam T, Tickle KS, Chen YP (2013) Computational intelligence for heart disease diagnosis: a medical knowledge driven approach. Expert Syst Appl 40:96–104

    Article  Google Scholar 

  8. Thakur JS, Prinja S, Garg CC, Mendis S, Menabde N (2011) Social and economic implications of non-communicable diseases in India. Indian J Commun Med 36:13–22

    Article  Google Scholar 

  9. Sheik Abdullah A, Selvakumar S, Abirami AM (2017) An introduction to data analytics: its types and its applications. Handbook of research on advanced data mining techniques and applications for business intelligence. IGI Global, Hershey

    Google Scholar 

  10. Liu H, Vinod K, Komandur ER, Saeed M, Joshua P, Sunghwan S, Yanshan W, Dingcheng L, Mojarad RM (2016) Toward a learning health-care system – knowledge delivery at the point of care empowered by big data and NLP. Biomed Inform Insights 1(13):13–22

    Google Scholar 

  11. Steyerberg EW (2009) Clinical prediction models. Stat Biol Health. https://doi.org/10.1007/978-0-387-77244-8

    Article  Google Scholar 

  12. Gupta R, Misra A (2007) Review: Type 2 diabetes in India: regional disparities. Brit J Diab Vasc Dis 7(1):12–16. https://doi.org/10.1177/14746514070070010301

    Article  Google Scholar 

  13. Venkat Narayan KM, Williamson DF (2009) Prevention of type 2 diabetes: risk status, clinic, and community. J Gen Intern Med 25(2):154–157

    Article  Google Scholar 

  14. Patil BM, Joshi RC, Toshniwal D (2010) Hybrid prediction model for type-2 diabetic patients. Expert Syst Applic 37(12):8102–8108. https://doi.org/10.1016/j.eswa.2010.05.078

    Article  Google Scholar 

  15. Mosca L, Benjamin EJ, Berra K, Bezanson JL, Dolor RJ, Lloyd-Jones DM, Newby LK, Pina IL, Roger VL, Shaw LJ, Zaho D (2011) Effectiveness-based guidelines for the prevention of cardiovascular disease in women – 2011 update. J Am Colleg Cardiol 57(12):1404–1423

    Article  Google Scholar 

  16. Meng XH, Huang YX, Rao DP, Zhang Q, Liu Q (2013) Comparison of three data mining models for predicting diabetes or pre-diabetes by risk factors. Kaohsiung J Med Sci 29(2):93–99

    Article  PubMed  Google Scholar 

  17. Carolyn E, Landis I, Abramson NW, Amodei N, Drews KL, Kaplan J, Levitt Katz LE, Lavietes S, Saletsky R, Seidman D, Yasuda P (2015) Longitudinal correlates of health risk behaviors in children and adolescents with type 2 diabetes. J Pediatr 166(5):1258–1264

    Article  Google Scholar 

  18. Lee BJ, Kim JY (2016) Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on machine learning. IEEE J Biomed Health Inform 20(1):39–46. https://doi.org/10.1109/jbhi.2015.2396520

    Article  PubMed  Google Scholar 

  19. Deniz A, Kiziloz HE, Dokeroglu T, Cosar A (2017) Robust multi-objective evolutionary feature subset selection algorithm for binary classification using machine learning techniques. Neuro Comp 241:128–146

    Google Scholar 

  20. Talaei-Khoeia A, Wilson JM (2018) Identifying people at risk of developing type 2 diabetes: a comparison of predictive analytics techniques and predictor variables. Int J Med Inform 119:22–38

    Article  Google Scholar 

  21. ShafenoorAmina M, Chiama YK, Varathanb KD (2019) Identification of significant features and data mining techniques in predicting heart disease. Telem Inform 36:82–93

    Article  Google Scholar 

  22. Sheik Abdullah A, Selvakumar S (2019) Assessment of the risk factors of type II diabetes using an improved combination of particle swarm optimization and decision trees by evaluation with fisher’s linear discriminant analysis. Soft Comput. https://doi.org/10.1007/s00500-018-3555-5

    Article  Google Scholar 

  23. Selvakumar S, Sheik Abdullah A, Suganya R (2019) Decision support system for type II diabetes and its risk factor prediction using bee based harmony search and decision tree algorithm. Int J Biomed Eng Technol 29(1):46–67. https://doi.org/10.1504/IJBET.2019.096880

    Article  Google Scholar 

  24. Huda S, Yearwood J, Jelinek HF, Hassan MM, Fortino G, Buckland M (2016) A hybrid feature selection with ensemble classification for imbalanced healthcare data: a case study for brain tumor diagnosis. IEEE Access 4:9145–9154

    Article  Google Scholar 

  25. Talbi EG (2004) Metaheuristics: from design to implementation. Wiley

    Google Scholar 

  26. Horst R, Tuy H (1996) Global optimization: deterministic approaches, 3rd edn. Springer-Verlag, Berlin

    Book  Google Scholar 

  27. Spall JC (2003) Introduction to stochastic search and optimization: estimation, simulation, and control. Wiley, Hoboken

    Book  Google Scholar 

  28. Lewis DD (1998) Naïve (Bayes) at forty: the independence assumption in information retrieval. Machine Learning: EMCL-98. Springer, Berlin, pp 4–15

    Chapter  Google Scholar 

  29. Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106

    Google Scholar 

  30. Marco D, Thomas S (2004) Ant colony optimization. Massachusetts Institute of Technology, Massachusetts

    Google Scholar 

  31. Dorigo M, Maniezzo V, Colorni A (1996) ‘Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Part B (Cybernet) 26(1):28–41. https://doi.org/10.1109/3477.484436

  32. Wong KC (2016) Computational biology and bioinformatics: gene regulation. CRC Press, Taylor & Francis group, Boca Raton

    Book  Google Scholar 

  33. Parpinelli RS, Lopes HS, Freitas AA (2002) Data mining with ant colony optimization algorithm. IEEE Trans Evol Comput 6(4):321–332

    Article  Google Scholar 

  34. Ando S, Iba H (2002) Ant algorithm for construction of evolutionary tree. In proceedings of the Genetic and Evolutionary conference, New York, pp 1552–1557

    Google Scholar 

  35. Talbi EG, Roux O, Fonlupt C (2001) Parallel ant colonies for quadratic assignment problem. Future Gener Comp syst 17(4):441–449

    Article  Google Scholar 

  36. Beni G, Wang J (1989) Swarm intelligence in cellular robotic systems. Proceedings of NATO Advanced Workshop on Robots and Biological Systems, Tuscany

    Google Scholar 

  37. Colorni A, Dorigo M, Maniezzo V (1994) Ant system for job-shops scheduling. Belgian J Oper Res Stat Comput Sci 34(1):39–53

    Google Scholar 

  38. Dorigo M, Luca M (1996) A study of some properties of ant-Q. Proceedings of the fourth international conference on parallel problem solving problem from nature. Springer Verlag, Berlin, pp 656–665

    Google Scholar 

  39. Li YJ, Wu TJ (2003) A nested hybrid ant colony algorithm for hybrid production scheduling problems. Acta Automatica Sinica 29(1):95–101

    Google Scholar 

  40. Gutjahr WJ (2000) A graph-based ant system and its convergence. Fut Gener Comp Syst 16(9):873–888

    Article  Google Scholar 

  41. Gamberdella LM, Dorigo M (1995) Ant-Q: a reinforcement learning approach to the travelling sales man problem. Proceedings of the 12th machine learning conference. Morgan Kaufmann, San Francisco, pp 252–260

    Google Scholar 

  42. Israel A, Wagner M, Lindenbaum AM, Bruckstein (1999) Distributed covering by ant-robots using evaporating traces. IEEE Trans Robot Autom 15(5):918–933. https://doi.org/10.1109/70.795795

  43. Krieger MJB, Billeter JB, Keller L (2000) Ant-like task allocation and recruitment in cooperative robots. Nature 406(31):992–995

    Article  CAS  PubMed  Google Scholar 

  44. Chu CH, Gu J, Hou X (2002) A heuristic ant algorithm for solving QoS multi-cast routing problem. Proceedings of the 2002 congress on evolutionary computation, Honolulu, pp 1630–1635

    Google Scholar 

  45. Sheik Abdullah A, Selvakumar S, Venkatesh M (2021) Assessment and evaluation of CHD risk factors using weighted ranked correlation and regression with data classification. Soft Comput 25(6):4979–5001. https://doi.org/10.1007/s00500-021-05663-y

    Article  Google Scholar 

  46. Myers JL, Arnold DW, Lorch RF (2003) Research design and statistical analysis, 2nd edn. Lawrence Erlbaum, New Jersey

    Book  Google Scholar 

  47. Nathan DM, Kuenen J, Borg R, Zheng H, Schoenfeld D, Heine RJ (2008) Translating the A1C assay into estimated average glucose values. Diab Care 31(8):1473–1478

    Article  CAS  Google Scholar 

  48. Qiang Y, Chen Wei-Neng, Zhengtao Y, Tianlong G, Yun L, Huaxiang Z, Jun Z (2017) Adaptive multimodal continuous ant colony optimization. IEEE Trans Evol Comput 21(2):191–205

    Article  Google Scholar 

  49. Huimin Z, Weitong G, Wu D, Meng S (2018) Study on an adaptive co-evolutionary ACO algorithm for complex optimization problems. Symmetry 10:104

    Article  Google Scholar 

  50. Baldi P, Brunak S, Chauvin Y, Andersen CAF, Nielsen H (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16(5):412–24. https://doi.org/10.1093/bioinformatics/16.5.412

    Article  CAS  PubMed  Google Scholar 

  51. Stone M (1974) Cross-validation choice and assessment of statistical predictions. J Royal Stat Soc Ser B (Methodological) 36(2):111–147

    Google Scholar 

  52. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 14:1137–1145

    Google Scholar 

  53. Sheik Abdullah A (2022) Assessment of risk factors in medical data using improved Binary Artificial Fish Swarm Algorithm with Classification upon Evaluation from F-Test. Int J Swarm Intell Res 13(1):0–0. https://doi.org/10.4018/IJSIR.2022010116

  54. Xue B, Zhang M, Browne WN (2014) Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms. Appl Soft Comp 18:261–276. https://doi.org/10.1016/j.asoc.2013.09.018

    Article  Google Scholar 

  55. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(2):179–88. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x

    Article  Google Scholar 

  56. Greiner M, Pfeiffer D, Smith RD (2000) Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 45(1–2):23–41. https://doi.org/10.1016/S0167-5877(00)00115-X

    Article  CAS  PubMed  Google Scholar 

  57. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837. https://doi.org/10.2307/2531595

    Article  CAS  PubMed  Google Scholar 

  58. Smith R, Slenning B (2000) Decision analysis: dealing with uncertainty in diagnostic testing. Prevent Vet Med 45(1–2):139–62. https://doi.org/10.1016/s0167-5877(00)00121-5

    Article  CAS  Google Scholar 

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  1. School of Computer Science Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

    A. Sheik Abdullah

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Appendix

Appendix

1.1 Appendix 1. Pseudocode of ACO-decision tree algorithm

Table 10

Table 10 Procedure of proposed ACO-decision tree model
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Table 11

Table 11 Attribute descriptions for type II diabetic data collected from hospital
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Abdullah, A.S. Assessment of the risk factors of type II diabetes using ACO with self-regulative update function and decision trees by evaluation from Fisher’s Z-transformation. Med Biol Eng Comput 60, 1391–1415 (2022). https://doi.org/10.1007/s11517-022-02530-2

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