Rule-based prediction of diabetes mellitus using a classification based on association rules

Abstract

Diabetes mellitus, a chronic metabolic disease, is characterised by persistently high blood sugar levels. It is projected that by 2030, the number of individuals with diabetes in developing nations would rise from roughly 84 million to 228 million, placing a substantial strain on healthcare systems. Therefore, there is a need for different predictions that can be used in early diagnosis, follow-up and preventive medicine for this disease. In this study, a data mining algorithm, the association classification approach, is used to classify diabetes on an open source dataset. The performance metrics of the model are accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value and F1-score values of 0.92, 0.78, 0.58, 0.98, 0.85, 0.93, 0.70 respectively. According to these results, the classification model based on association rules is highly successful in classifying diabetes melitus. In addition, as an output of the model, certain rules are proposed that can be used in early diagnosis, treatment and preventive medicine of diabetes mellitus.

Keywords

• Diabetes mellitus
• Association rules
• Associative Classification
• Classification

References

1. REFERENCES
2. [1] T. Akinyemiju, S. Abera, M. Ahmed, N. Alam, M. A. Alemayohu, C. Allen, et al., (2017) The burden of primary liver cancer and underlying etiologies from 1990 to 2015 at the global, regional, and national level: results from the global burden of disease study 2015 JAMA oncology, vol. 3, pp. 1683-1691.
3. [2] P. R. Galle, A. Forner, J. M. Llovet, V. Mazzaferro, F. Piscaglia, J.-L. Raoul, et al. (2018) EASL clinical practice guidelines: management of hepatocellular carcinoma. Journal of hepatology, vol. 69, pp. 182-236.
4. [3] H. B. El–Serag and K. L. Rudolph (2007) Hepatocellular carcinoma: epidemiology and molecular carcinogenesis. Gastroenterology, vol. 132, pp. 2557-2576.
5. [4] Z. Ding, N. Wang, N. Ji, and Z.-S. Chen (2022) Proteomics technologies for cancer liquid biopsies. Molecular Cancer, vol. 21, p. 53.
6. [5] S. Aksoy, M. ÖZAVSAR, A. ALTINDAL (2022) Classification of VOC Vapors Using Machine Learning Algorithms Journal of Engineering Technology and Applied Sciences, vol. 7, pp. 97-107.
7. [6] W. Naboulsi, D. A. Megger, T. Bracht, M. Kohl, M. Turewicz, M. Eisenacher, et al. (2016) Quantitative tissue proteomics analysis reveals versican as potential biomarker for early-stage hepatocellular carcinoma. Journal of proteome research, vol. 15, pp. 38-47.
8. [7] M. Schonlau and R. Y. Zou (2020) The random forest algorithm for statistical learning. The Stata Journal, vol. 20, pp. 3-29.

9. [8] F. Tang and H. Ishwaran (2017) Random forest missing data algorithms. Statistical Analysis and Data Mining: The ASA Data Science Journal, vol. 10, pp. 363-377.
10. [9] V. Fonti and E. Belitser (2017) Feature selection using lasso. vol. 30, pp. 1-25..
11. [10] T. Kimhofer, H. Fye, S. Taylor-Robinson, M. Thursz, and E. Holmes (2015) Proteomic and metabonomic biomarkers for hepatocellular carcinoma: a comprehensive review. British journal of cancer, vol. 112, pp. 1141-1156.
12. [11] X. Zheng, Q. Peng, L. Wang, X. Zhang, L. Huang, J. Wang, et al. (2020) Serine/arginine-rich splicing factors: the bridge linking alternative splicing and cancer. International journal of biological sciences, vol. 16, p. 2442.
13. [12] R. Karni, E. de Stanchina, S. W. Lowe, R. Sinha, D. Mu, and A. R. Krainer (2007) The gene encoding the splicing factor SF2/ASF is a proto-oncogene. Nature structural & molecular biology, vol. 14, pp. 185-193.
14. [13] O. Anczuków, M. Akerman, A. Cléry, J. Wu, C. Shen, N. H. Shirole, et al. (2015) SRSF1-regulated alternative splicing in breast cancer. Molecular cell, vol. 60, pp. 105-117.
15. [14] F. J. de Miguel, R. D. Sharma, M. J. Pajares, L. M. Montuenga, A. Rubio, and R. Pio (2014) Identification of alternative splicing events regulated by the oncogenic factor SRSF1 in lung cancer. Cancer research, vol. 74, pp. 1105-1115.
16. [15] C. Ghigna, S. Giordano, H. Shen, F. Benvenuto, F. Castiglioni, P. M. Comoglio, et al. (2005) Cell motility is controlled by SF2/ASF through alternative splicing of the Ron protooncogene," Molecular cell, vol. 20, pp. 881-890.
17. [16] J. Long, Z.-W. Lang, H.-G. Wang, T.-L. Wang, B.-E. Wang, and S.-Q. Liu (2010) Glutamine synthetase as an early marker for hepatocellular carcinoma based on proteomic analysis of resected small hepatocellular carcinomas. Hepatobiliary Pancreat Dis Int, vol. 9, pp. 296-305.