Development of a Model for Control the Flow of Patients with Cardiovascular Diseases Using Data Mining Methods
DOI:
https://doi.org/10.14529/ctcr200210Keywords:
logistic regression, decision trees, random forest, cardiovascular diseases, learning algorithmsAbstract
Introduction. Currently, the development of Big Data technologies and methods of big data mining has opened up the possibility of investigating the timeliness, availability and effectiveness of therapy when processing all available information about the treatment practice. Personalized and preventive medicine methods based on remote monitoring of patients and intelligent analysis of similar treatment practices will lead to significant cost savings and improved quality of life. One of the most effective methods of studying patient data and their electronic medical records is machine learning methods.
Aim. This study is aimed at building a model for managing the flow of patients with cardiovascular diseases based on the analysis of personalized patient data maps.
Materials and methods. The forecast for treatment of patients with heart diseases was determined using the method of logistic regression, the algorithm for building ID3 decision trees, and the method of training the ensemble – random forests. As part of the experimental study, the effectiveness of the methods considered for forecasting was evaluated based on the analysis of the ROC curve and the AUC metric.
Results. Experiments on an array of electronic personalized data about medical services in the territorial Fund of compulsory medical insurance (TFOMS) and the medical information and analytical center of Orenburg showed that for short-term forecasting for 1 month, the ID3 algorithm for constructing decision trees showed better results, and when the period under consideration was increased to 3 months, the method of logistic regression was more effective.
Conclusion. The proposed approach to predicting patient requests allows us to improve the quality of management of the clinical and organizational health care system in the provision of medical care, as well as to plan the volume and number of individual medical services.
References
Windmann, S. Big Data Analysis of Manufacturing Processes / S. Windmann, A. Maier, O. Niggemann, C. Frey // Journal of Physics: Conference Series. – 2015. – DOI: 10.1088/1742-6596/659/1/012055
Kim, G. Big Data Applications in the Government Sector: A Comparative Analysis among Leading Countries / G. Kim, J. Chung // Communications of the ACM. – 2014. – Vol. 57. – P. 78–85. DOI: 10.1145/2500873
Alshura, M. Big Data in Marketing Arena. Big Opportunity, Big Challenge, and Research Trends: An Integrated View / M. Alshura, A. Zabadi, M. Abughazaleh // MANAGEMENT AND ECONOMICS REVIEW. – 2018. – Vol. 3. DOI: 10.24818/mer/2018.06-06
Beam, A. Big Data and Machine Learning in Health Care / A. Beam, I. Kohane // JAMA. – 2018. – Vol. 319. DOI: 10.1001/jama.2017.18391
Patient-Level Effectiveness Prediction Modeling for Glioblastoma Using Classification Trees / T. Geldof, N. Damme, I. Huys, W. Dyck // Frontiers in Pharmacology. – 2020. – Vol. 10. DOI: 10.3389/fphar.2019.01665
Blasiak, A. CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence / A. Blasiak, J. Khong, T. Kee // SLAS TECHNOLOGY: Translating Life Sciences Innovation. – 2019. – DOI: 10.1177/2472630319890316
Lin, R. Diseases and Health Monitoring Big Data: A Survey. / R. Lin, Z. Chronic, H. Wang // IEEE Reviews in Biomedical Engineering. – 2018. DOI: 10.1109/RBME.2018.2829704
Leung, K. Application of Big Data in Decision Making for Emergency Healthcare Management / K. Leung, A. Stevenson // International Journal of Re-search and Engineering. – 2018. – Vol. 5. – P. 311–314. DOI: 10.21276/ijre.2018.5.2.2
Зимина, Е.Ю. Применение облачных технологий в задачах математического анализа кардиологической информации / Е.Ю. Зимина, М.А. Новопашин, А.В. Шмид // Сборник трудов IV международной конференции и молодежной школы «Информационные технологии и нанотехнологии» (ИТНТ-2018). – Самара, 2018. – С. 2282–2287.
Зимина, E.Ю. Кластерный анализ кардиологических данных / E.Ю. Зимина // Статистика и Экономика. – 2018. – Т. 15, № 2. – С. 30–37. DOI: 10.21686/2500-3925-2018-2-30-37
Бурыкин, И.М. Перспективность метода анализа больших данных (big data) для оценки качества и эффективности фармакотерапии пациентов с артериальной гипертензией / И.М. Бурыкин, Г.Н. Алеева, Р.Х. Хафизьянова // Современные технологии в медицине. – 2017. – № 9. – С. 194. DOI: 10.17691/stm2017.9.4.24
Баранов, A.А. Методы и средства комплексного интеллектуального анализа медицинских данных / A.А. Баранов // Труды Института системного анализа Российской академии наук. – 2015. – Т. 65, № 2. – С. 81–93.
Степанян, И.В. Научно-методические основы и биоинформационные технологии управления профессиональными рисками в медицине труда: дис. … д-ра биол. наук / И.В. Степанян. – М., 2012.
Weng, S. Can Machine-learning improve cardiovascular risk prediction using routine clinical data / S. Weng, J. Reps, J. Kai. // PLoS ONE. – 2017. – Vol. 12. DOI: 10.1371/journal.pone.0174944
Krishnan, S. Application of Analytics to Big Data in Healthcare / S. Krishnan // 32nd Southern Biomedical Engineering Conference. – 2016. – P. 156–157. DOI: 10.1109/SBEC.2016.88
Tocci, G. Use of Electronic Support for Implementing Global Cardiovascular Risk Management / G. Tocci, A. Ferrucci, P. Guida // High HEART Press Cardiovasc Prev. – 2010. – Vol. 17. – P. 37–47. DOI: 10.2165/11311750-000000000-00000
