Acta Informatica Pragensia 2020, 9(2), 92-107 | DOI: 10.18267/j.aip.1356688

Mild Cognitive Impairment Detection Using Association Rules Mining

Franti¹ek Babič ORCID...1, „udmila Pusztovį1, Ljiljana Trtica Majnarię ORCID...2,3
1 Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Ko¹ice, Letnį 9, 042 00 Ko¹ice, Slovak Republic
2 Department of Public health, Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, Crkvena 21, 31000 Osijek, Croatia
3 Department of Internal medicine, Family Medicine and the History of Medicine, Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, Josipa Huttlera 4, 31000 Osijek, Croatia

A single Mild cognitive impairment (MCI) is a transitional state between normal cognition and dementia. The typical diagnostic procedure relies on neuropsychological testing, which is insufficiently accurate and does not provide information on patients’ clinical profiles. The objective of this paper is to improve the recognition of elderly primary care patients with MCI by using an approach typically applied in the market basket analysis – association rules mining. In our case, the association rules represent various combinations of the clinical features or patterns associated with MCI. The analytical process was performed in line with the CRISP-DM, the methodology for data mining projects widely used in various research or industry domains. In the data preparation phase, we applied several approaches to improve the data quality like the k-Nearest Neighbour, correlation analysis, Chi Merge and K-Means algorithms. The analytical solution“s success was confirmed not only by the novelty and correctness of new knowledge, but also by the form of visualization that is easily understandable for domain experts. This iterative approach provides a set of rules (patterns) that meet minimum support and reliability. The extracted rules may help medical professionals recognize clinical patterns; however, the final decision depends on the expert. A medical expert has a crucial role in this process by enabling the link between the information contained in the rules and the evidence-based knowledge. It markedly contributes to the interpretability of the results.

Keywords: Association Rules, Patterns, Mild Cognitive Impairment, Interpretability

Received: June 29, 2020; Revised: August 19, 2020; Accepted: August 20, 2020; Prepublished online: August 20, 2020; Published: December 31, 2020  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Babič, F., Pusztovį, „., & Majnarię, L.T. (2020). Mild Cognitive Impairment Detection Using Association Rules Mining. Acta Informatica Pragensia9(2), 92-107. doi: 10.18267/j.aip.135
Download citation

References

  1. Aevarsson, O., & Skoog, I. (2000). A longitudinal population study of the mini-mental state examination in the very old: relation to dementia and education. Dementia and Geriatric Cognitive Disorders, 11(3), 166-175. https://doi.org/10.1159/000017231 Go to original source...
  2. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases, (pp. 487-499). VLDB.
  3. Albert, M.S., DeKosky, S.T., Dickson, D.W., Dubois,B., Feldman, H.H., Fox, N.C., Gamst, A., Holtzman, D.M., Jagust, W.T., Petersen,R.C., et al. (2011). The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's and Dementia, 7(3), 270-279. https://doi.org/10.1016/j.jalz.2011.03.008 Go to original source...
  4. Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175-185. https://doi.org/10.2307/2685209 Go to original source...
  5. Alwidian, J., Hammo, B. H., & Obeid, N. (2018). WCBA: Weighted classification based on association rules algorithm for breast cancer disease. Applied Soft Computing, 62, 536-549. https://doi.org/10.1016/j.asoc.2017.11.013 Go to original source...
  6. Antonopoulos, A.S., Margaritis, M., Lee, R., Chanook, K., & Antoniades, C. (2012). Statins as anti-inflammatory agents in atherogenesis: Molecular mechanisms and lessons from the recent clinical trials. Current Pharmaceutical Design, 8(11), 1519-1530. https://doi.org/10.2174/138161212799504803 Go to original source...
  7. Avila-Funes, J.A., Amieva, H., Barberger-Gateau, P., Le-Goff, M., Raoux, N., Ritchie, K., Carriere, I., Tavernier, B., Tzourio, C., Gutierrez-Robledo, L.M. et al. (2009). Cognitive impairment improves the predictive validity of the phenotype of frailty for adverse health outcomes: the three-city study. Journal of the American Geriatrics Society, 57, 453-461. https://doi.org/10.1111/j.1532-5415.2008.02136.x Go to original source...
  8. Boban, M., Malojcic, B., Mimica, N., Vukovię, S., Hof, P.R., & Simię, G. (2012). The reliability and validity of the Mini-Mental State Examination in the elderly Croatian population. Dementia and Geriatric Cognitive Disorders, 33, 385-392. https://doi.org/10.1159/000339596 Go to original source...
  9. Borah, A., & Nath, B. (2018). Identifying risk factors for adverse diseases using dynamic rare association rule mining. Expert Systems with Applications, 33, 233-263. https://doi.org/10.1016/j.eswa.2018.07.010 Go to original source...
  10. Buchanan, A. V. (2006). Dissecting Complex Disease: The Quest for the Philosopher's Stone? International Journal of Epidemiology, 35(3), 562-571. https://doi.org/10.1093/ije/dyl001 Go to original source...
  11. Bugnicourt, J. M., Godefroy, O., Chillon, J. M., Choukroun, G. & Massy, Z. A. (2013). Cognitive disorders and dementia in CKD: The neglected kidney-brain axis. Journal of the American Society of Nephrology, 24 (3), 353-363. https://doi.org/10.1681/ASN.2012050536 Go to original source...
  12. Cavagna, L., Boffini, N., Cagnotto, G., Inverardi, F., Grosso, V., & Caporali, R. (2012). Atherosclerosis and Rheumatoid Arthritis: More Than a Simple Association. Mediators of Inflammation, 2012, ID 147354. https://doi.org/10.1155/2012/147354 Go to original source...
  13. Deleidi, M., Jäggle, M., & Rubino, G. (2015). Immune aging, dysmetabolism, and inflammation in neurological diseases. Frontiers in Neuroscience, 9, 172-178. https://doi.org/10.3389/fnins.2015.00172 Go to original source...
  14. Futagami, S., Takahashi, H., Norose, Y., & Kobayashi, M. (1998). Systemic and local immune responses against Helicobacter pylori urease in patients with chronic gastritis, distinct IgA and IgG productive sites. Gut, 43(2), 168-175. https://doi.org/10.1136/gut.43.2.168 Go to original source...
  15. Harap, M., Husein, A. M., Aisyah, S., Lubis, F. R., & Wijaya, B. A. (2018). Mining association rule based on the disease population for recommendation of medicine need. Journal of Physics: Conference Series, 1007(1). https://doi.org/10.1088/1742-6596/1007/1/012017 Go to original source...
  16. Hogervorst, E., Huppert, F., Matthews, F. E., & Brayne, C. (2010). Thyroid function and cognitive decline in the MRC Cognitive Function and Ageing Study. Psychoneuroendocrinology, 33(7), 1013-1022. https://doi.org/10.1016/j.psyneuen.2008.05.008 Go to original source...
  17. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. R., & Wirth, R. (2000). CRISP-DM 1.0: Step-by-step data mining guide.
  18. Jain, S., Gautam, V., & Naseem, S. (2011). Acute-phase proteins: As diagnostic tool. Journal of Pharmacy and BioAllied Sciences, 3(1), 118-127. https://doi.org/10.4103/0975-7406.76489 Go to original source...
  19. Kerber, R. (1992). ChiMerge: Discretization of Numeric Attributes. In AAAI'92 Proceedings of the Tenth National Conference on Artificial Intelligence (pp. 123-128). ACM.
  20. Lakshmi, K. S., & Vadivu, G. (2017). Extracting Association Rules from Medical Health Records Using Multi-Criteria Decision Analysis. Procedia Computer Science, 115, 290-295. https://doi.org/10.1016/j.procs.2017.09.137 Go to original source...
  21. Lazarczyk, M. J., Hof, P. R., Bouras, C., & Giannakopoulos, P. (2012). Preclinical Alzheimer Disease: Identification of Cases at Risk among Cognitively Intact Older Individuals. BMC Medicine, 10(1), 127-135. https://doi.org/10.1186/1741-7015-10-127 Go to original source...
  22. Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. https://doi.org/10.1109/TIT.1982.1056489 Go to original source...
  23. Qiu, S., Chang, G. H., Panagia, M., Gopal, D. M., Au, R., & Kolachalama, V. B. (2018). Fusion of deep learning models of MRI scans, Mini-Mental State Examination, and logical memory test enhances diagnosis of mild cognitive impairment. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 10(1), 737-749. https://doi.org/10.1016/j.dadm.2018.08.013 Go to original source...
  24. Postiglione, A., Milan, G., Ruocco, A., Gallotta, G., Guiotto, G., & Di Minno, G. (2001). Plasma Folate, Vitamin B (12), and Total Homocysteine and Homozygosity for the C677T Mutation of the 5,10-Methylene Tetrahydrofolate Reductase Gene in Patients with Alzheimer's Dementia. A Case-Control Study. Gerontology, 47(6), 324-329. https://doi.org/10.1159/000052822 Go to original source...
  25. Roberts, R. O., Geda, Y. E., Knopman, D. S., Boeve, B. F., Christianson, T. J., Pankratz, V. S., Kullo, I. J., Tangalos, E. G., & Petersen, R. C. (2009). Association of C-Reactive Protein with Mild Cognitive Impairment. Alzheimer's & Dementia, 5(5), 398-405. https://doi.org/10.1016/j.jalz.2009.01.025 Go to original source...
  26. Sariyer, G., & Öcal Taŗar, C. (2020). Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining. Health Informatics Journal, 26(2), 1177-1193. https://doi.org/10.1177/1460458219871135 Go to original source...
  27. Shearer, C. (2000). Strategic Modeling for the Characterization of the Conditions That Allow the Anticipation of the Consumer's Requests. The CRISP-DM Model: The New Blueprint for Data Mining. Journal of Data Ware-housing, 5, 13-22. https://doi.org/10.4236/jss.2015.310021 Go to original source...
  28. Schrijvers, E. M. C., Witteman, J. C. M., Sijbrands, E. J. G., Hofman, A., Koudstaal, P. J., & Breteler, M. M. B. (2010). Insulin Metabolism and the Risk of Alzheimer Disease: The Rotterdam Study. Neurology, 75(22), 1982-1987. https://doi.org/10.1212/WNL.0b013e3181ffe4f6 Go to original source...
  29. Tjia, J., Velten, S.J., Parsons, C., Valluri, S., & Briesacher, B.A. (2010). Studies to reduce unnecessary medication use in frail older adults: a systematic review. Drugs Aging, 30, 285-307. https://doi.org/10.1007/s40266-013-0064-1 Go to original source...
  30. Umegaki, H. (2014). Type 2 Diabetes as a Risk Factor for Cognitive Impairment: Current Insights. Clinical Interventions in Aging, 9, 1011-1019. https://doi.org/10.2147/CIA.S48926 Go to original source...
  31. Van Guldener, C. (2006). Why Is Homocysteine Elevated in Renal Failure and What Can Be Expected from Homocysteine-Lowering? Nephrology Dialysis Transplantation, 21(5), 161-1166. https://doi.org/10.1093/ndt/gfl044 Go to original source...
  32. Walker, S. R., Wagner, M., & Tangri, N. (2014). Chronic kidney disease, frailty and successful aging: a review. Journal of Renal Nutrition, 24(6), 364-370. https://doi.org/10.1053/j.jrn.2014.09.001 Go to original source...
  33. Whaley-Connell, A., & Sowers, J.R. (2018). Insulin resistance in kidney disease: Is there a distinct role separate from that of diabetes or obesity. Cardiorenal Medicine, 8(1), 41-49. https://doi.org/10.1159/000479801 Go to original source...
  34. Whitmer, R. A. (2007). The Epidemiology of Adiposity and Dementia. Current Alzheimer Research, 4(2), 117-122. https://doi.org/10.2174/156720507780362065 Go to original source...
  35. Zhang, X., Hu B., Ma X., Moore P. & Chen, J. (2014). Ontology Driven Decision Support for the Diagnosis of Mild Cognitive Impairment. Computer Methods and Programs in Biomedicine, 113(3), 781-791. https://doi.org/10.1016/j.cmpb.2013.12.023 Go to original source...

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.