An Intelligent Multicriteria Model for Diagnosing Dementia in People Infected with Human Immunodeficiency Virus
Source abstract
Abstract
Hybrid models to detect dementia based on Machine Learning can provide accurate diagnoses in individuals with neurological disorders and cognitive complications caused by Human Immunodeficiency Virus (HIV) infection. This study proposes a hybrid approach, using Machine Learning algorithms associated with the multicriteria method of Verbal Decision Analysis (VDA). Dementia, which affects many HIV-infected individuals, refers to neurodevelopmental and mental disorders. Some manuals standardize the information used in the correct detection of neurological disorders with cognitive complications. Among the most common manuals used are the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, 5th edition) of the American Psychiatric Association and the International Classification of Diseases, 10th edition (ICD-10)—both published by World Health Organization (WHO). The model is designed to explore the predictive of specific data. Furthermore, a well-defined database data set improves and optimizes the diagnostic models sought in the research.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: HIV Research and Treatment · Alzheimer's disease research and treatments · Dementia and Cognitive Impairment Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate Program in Clinical Care in Nursing and Health, Ceará State University, Fortaleza 60714-903, Brazil
Graduate Program in Applied Informatics, University of Fortaleza (UNIFOR), Fortaleza 60811-905, Brazil
Porto School of Nursing, 4200-072 Porto, Portugal
Funding
National Counsel of Technological and Scientific Development (CNPq)
References (77)
Show source-provided references
1. Li, Y., Jiang, J., Shen, T., Wu, P., and Zuo, C. (2018, January 18–21). Radiomics features as predictors to distinguish fast and slow progression of Mild Cognitive Impairment to Alzheimer’s disease. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.10.1109/EMBC.2018.8512273 ↗
2. Early prediction of putamen imaging features in HIV-associated neurocognitive impairment syndrome10.1186/s12883-021-02114-x ↗
3. Changing clinical phenotypes of HIV-associated neurocognitive disorders10.1007/s13365-017-0556-6 ↗
4. HIV as a chronic disease: Evaluation and management of non-AIDS defining, Management of HIV-Associated Disease in HIV
5. HIV-associated neurocognitive disorder—pathogenesis and prospects for treatment10.1038/nrneurol.2016.27 ↗
6. Rom, S., Gajghate, S., Winfield, M., Reichenbach, N.L., and Persidsky, Y. (2020). Combination of HIV-1 and Diabetes Enhances Blood Brain Barrier Injury via Effects on Brain Endothelium and Pericytes. Int. J. Mol. Sci., 21.10.3390/ijms21134663 ↗
7. Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean, and how will they impact health care?10.1016/j.arth.2018.02.067 ↗
8. Tamanini, I. (2015). Hybrid Approaches of Verbal Decision Analysis Methods. [Ph.D. Thesis, University of Fortaleza].
9. Hjalmarsson, V. (2018). Machine learning and Multi-criteria decision analysis in healthcare: A comparison of machine learning. [Ph.D. Thesis, Mid Sweden University].
10. Towards the applied hybrid model in decision making: A neuropsychological diagnosis of alzheimer’s disease study case
11. A framework for selecting machine learning models using TOPSIS
12. Automated cognitive workload assessment using logical teaching learning-based optimization and promethee multi-criteria decision making approach10.1109/JSEN.2020.3006486 ↗
13. Predicting diabetes mellitus with machine learning techniques using multi-criteria decision making
14. Towards the Applied Hybrid Model In Decision Making: Support the Early Diagnosis of Type 2 Diabetes10.1007/978-3-642-34062-8_84 ↗
15. Evaluation of the Alzheimer’s disease clinical stages under the optics of hybrid approaches in Verbal decision analysis10.1016/j.tele.2017.04.008 ↗
16. A more human-like portfolio optimization approach10.1016/j.ejor.2016.06.018 ↗
17. Gurgel, S.N. (2014). Vulnerability to HIV/AIDS in the Elderly: A Comparative Study. [Master’s Thesis, Federal University of Paraiba].
18. UNAIDS (2019). Joint united nations program on HIV/AIDS. Communities at the centre: Defending rights, breaking barriers, reaching people with HIV services. Glob. AIDS, Available online: https://www.unaids.org/sites/default/files/media_asset/2019-global-AIDS-update_en.pdf.
19. Dementia prevention, intervention, and care: 2020 report of the lancet commission10.1016/S0140-6736(20)30367-6 ↗
20. Updates on cortical dementia in HIV-infected individuals
21. Updated research nosology for HIV-associated neurocognitive disorders10.1212/01.WNL.0000287431.88658.8b ↗
22. Neuropsychiatric manifestations of HIV infection and AIDS
23. The effect of central nervous system penetration effectiveness of highly active antiretroviral therapy on neuropsychological performance and neuroimaging in HIV infected individuals10.1007/s11481-015-9610-4 ↗
24. AIDS dementia complex and primary HIV brain infection10.1016/0165-5728(88)90144-0 ↗
25. AIDS dementia complex I: Clinical features10.1002/ana.410190602 ↗
26. Association of brain structure changes and cognitive function with combination antiretroviral therapy in HIV-positive individuals10.1001/jamaneurol.2017.3036 ↗
27. Cerebrospinal fluid analysis in the HIV infection and compartmentalization of HIV in the central nervous system10.1590/0004-282X20150071 ↗
28. Could antiretroviral neurotoxicity play a role in the pathogenesis of cognitive impairment in treated HIV disease?10.1097/QAD.0000000000000538 ↗
29. Cognitive dysfunction in HIV patients despite long-standing suppression of viremia10.1097/QAD.0b013e3283354a7b ↗
30. Working Group of the America Academy of Neurology AIDS Task Force (1991). Nomenclature and research case definitions for neurologic manifestations of human immunodeficiency virus type 1(HIV-1) infection. Neurology, 41, 778–785.10.1212/WNL.41.6.778 ↗
31. Trastornos neurocognitivos en pacientes VIH positivos Datos preliminares de una cohorte prospectiva uruguaya
32. Classification and regression trees10.1002/widm.8 ↗
33. Consistency of random forests
34. Bagging predictors10.1007/BF00058655 ↗
35. Beğenilmiş, E., and Uskudarli, S. (2018, January 25–27). Classification of organized behavior of sets of tweets using supervised learning methods. Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics (WIMS ’18), Association for Computing Machinery, Novi Sad, Serbia.10.1145/3227609.3227665 ↗
36. Pant, A. (2021, February 21). Introduction to Logistic Regression. Average. Towards Data Science. Available online: https://towardsdatascience.com/introduction-to-logistic-regression-66248243c148.
37. A Protocol for the Diagnosis of Autism Spectrum Disorder Structured in Machine Learning and Verbal Decision Analysis10.1155/2021/1628959 ↗
38. Han, D., Ma, L., and Yu, C. (2008, January 12–14). Financial prediction: Application of logistic regression with factor analysis. Proceedings of the 2008 4th International Conference on Wireless Communications, Networks and Mobile Computing, Dalian, China.10.1109/WiCom.2008.2308 ↗
39. Applying logistic regression models to assess household financial decisions regarding debt10.1016/j.procs.2020.09.055 ↗
40. AVT—Vidhya Analytics Team (2021, February 15). Simple Guide to Logistic Regression in R and Python. Available online: https://www.analyticsvidhya.com/blog/2015/11/beginners-guide-on-logistic-regression-in-r/.
41. Andrew, N.G. (2021, February 15). CS229 Lecture Notes. Available online: http://cs229.stanford.edu/syllabus.html.
42. Bacaer, N. (2010). Verhulst and the logistic equation. A Short History of Mathematical Population Dynamics, Springer. [2011th ed.].10.1007/978-0-85729-115-8_6 ↗
43. To explain or predict?10.1214/10-STS330 ↗
44. The elements of statistical learning: Data mining, inference, and prediction10.1007/BF02985802 ↗
45. Introduction to the logistic regression model
46. Sample size determination for logistic regression10.1016/j.cam.2013.06.031 ↗
47. Liu, L., and Özsu, M.T. (2009). Bayes classifier. Encyclopedia of Database Systems, Springer.10.1007/978-0-387-39940-9 ↗
48. (2021, February 15). Lund’s Research Binomial. Binomial Logistic Regression Using SPSS Statistics. Available online: https://statistics.laerd.com/spss-tutorials/binomial-logistic-regression-using-spss-statistics.php.
49. Learning Probabilistic Networks, Philips research laboratories10.1017/S0269888998004019 ↗
50. Larichev, O.I., and Moshkovich, H.M. (1997). Verbal Decision Analysis for Unstructured Problems, Kluwer Academic Press.10.1007/978-1-4757-2638-1 ↗
51. Verbal decision analysis applied on the optimization of alzheimer’s disease diagnosis: A case study based on neuroimaging10.1007/978-1-4419-7046-6_56 ↗
52. Reducing incomparability in multicriteria decision analysis: An extension of the zapros methods10.1590/S0101-74382011000200004 ↗
53. Tamanini, I., and Pinheiro, P.R. (2001). Challenging the incomparability problem: An approach methodology based on zapros. International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences, Springer.
54. Ranking multicriteria alternatives: The method ZAPROS III10.1016/S0377-2217(00)00096-5 ↗
55. Ordinal judgments in multiattribute decision analysis10.1016/S0377-2217(01)00106-0 ↗
56. Tamanini, I., Pinheiro, P.R., and dos Santos, C.N. (2012). A hybrid approach to verbal decision analysis and machine learning. International Conference on Rough Sets and Current Trends in Computing, Springer.10.1007/978-3-642-32115-3_14 ↗
57. Castro, A.K.A., Pinheiro, P.R., and Pinheiro, M.C.D. (2008, January 23–25). A hybrid model for aiding in decision making for the neuropsychological diagnosis of Alzheimer’s disease, RSCTC ’08. Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing, Akron, OH, USA.
58. A new machine learning model based on induction of rules for autism detection10.1177/1460458218824711 ↗
59. Application of data mining algorithms for dementia in people with HIV/AIDS10.1155/2021/4602465 ↗
60. Random forest versus logistic regression: A large-scale benchmark experiment
61. Orange: Data mining toolbox in python
62. (2021, April 11). CIF, 2001. International Classification of Functioning, Disability and Health. Available online: https://psychiatr.ru/download/1313?view=name=CF_18.pdf.
63. Tamanini, I., Pinheiro, P.R., and Carvalho, A.L. (2007). Aranaú Software: A New Tool of the Verbal Decision Analysis, University of Fortaleza. Technical Report.
64. Representações e práticas de cuidado de profissionais de saúde às pessoas com HIV10.1590/s1980-220x2018017903478 ↗
65. Scutari, R., Alteri, C., Perno, C.F., Svicher, V., and Aquaro, S. (2017). The role of HIV infection in neurologic injury. Brain Sci., 7.10.3390/brainsci7040038 ↗
66. HIV-associated neurocognitive disorder and HIV-associated myelopathy in a patient with a preserved CD4, but high viral load-a rarely reported phenomenon: A case report and literature review10.1186/s12879-020-05297-9 ↗
67. Ceccarelli, G., Borrazzo, C., Lazzaro, A., Innocenti, G.P., Celani, L., Cavallari, E.M., Pinacchio, C., Santinelli, L., Mastroianni, C.M., and dÉttorre, G. (2019). Problems in diagnosing asymptomatic neurosyphilis in HIV-positive patients: A retrospective study. Brain Sci., 9.10.3390/brainsci9100278 ↗
68. Purinergic signaling in infectious diseases of the central nervous system10.1016/j.bbi.2020.07.026 ↗
69. Neuroinfectious diseases10.1001/jamaneurol.2014.3442 ↗
70. Cognitive impairment in people living with HIV in the ART era: A review10.1093/bmb/ldy019 ↗
71. On the road to HIV/AIDS competence in the home: Building a health-friendly environment for people living with HIV/AIDS10.3390/ijerph120303264 ↗
72. Current perspectives on HIV-1 antiretroviral drug resistance10.3390/v6104095 ↗
73. Safety considerations in drug treatment of depression in HIV-positive patients: An updated review10.2165/11592070-000000000-00000 ↗
74. De Francesco, D. (2016, January 23–26). Cognitive function and depression in HIV-positive individuals and matched controls. Proceedings of the Presented at the National AIDS Treatment Advocacy Project Conference, Glasgow, Scotland.
75. Microglia have a protective role in the development of viral encephalitis-induced seizures and hippocampal damage10.1016/j.bbi.2018.09.006 ↗
76. Prevention, intervention and care for dementia: 2020 Lancet Commission report10.1016/S0140-6736(20)30367-6 ↗
77. Quality of life of people living with HIV/AIDS: Temporal, socio-demographic and perceived health relationship 110.1590/1518-8345.1258.2874 ↗