Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic Diseases
This paper discusses a methodology to improve the prevention processes of chronic diseases such as diabetes and strokes. The research motivation is to find a new methodological approach to design advanced Diagnostic and Therapeutic Care Pathways (PDTAs) based on the prediction of chronic disease usi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
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| Series: | Eng |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4117/5/1/15 |
| _version_ | 1797241224886747136 |
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| author | Angelo Rosa Alessandro Massaro |
| author_facet | Angelo Rosa Alessandro Massaro |
| author_sort | Angelo Rosa |
| collection | DOAJ |
| description | This paper discusses a methodology to improve the prevention processes of chronic diseases such as diabetes and strokes. The research motivation is to find a new methodological approach to design advanced Diagnostic and Therapeutic Care Pathways (PDTAs) based on the prediction of chronic disease using telemedicine technologies and machine learning (ML) data processing techniques. The aim is to decrease health risk and avoid hospitalizations through prevention. The proposed method defines a Process Mining Organization (PMO) model, managing risks using a PDTA structured to prevent chronic risk. Specifically, the data analysis is focused on stroke risk. First, we applied and compared the Random Forest (RF) and Gradient Boosted Trees (GBT) supervised algorithms to predict stroke risk, and then, the Fuzzy c-Means unsupervised algorithm to cluster information on the predicted results. The application of the proposed approach is able to increase the efficiency of healthcare human resources and drastically decrease care costs. |
| first_indexed | 2024-04-24T18:19:56Z |
| format | Article |
| id | doaj.art-28bc27c6a34848eb9aa15a1495fd11b9 |
| institution | Directory Open Access Journal |
| issn | 2673-4117 |
| language | English |
| last_indexed | 2024-04-24T18:19:56Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Eng |
| spelling | doaj.art-28bc27c6a34848eb9aa15a1495fd11b92024-03-27T13:35:59ZengMDPI AGEng2673-41172024-02-015128230010.3390/eng5010015Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic DiseasesAngelo Rosa0Alessandro Massaro1Department of Management, Finance and Technology, LUM, Libera Università Mediterranea “Giuseppe Degennaro”, S.S. 100-Km.18, Parco il Baricentro, 70010 Bari, ItalyDepartment of Management, Finance and Technology, LUM, Libera Università Mediterranea “Giuseppe Degennaro”, S.S. 100-Km.18, Parco il Baricentro, 70010 Bari, ItalyThis paper discusses a methodology to improve the prevention processes of chronic diseases such as diabetes and strokes. The research motivation is to find a new methodological approach to design advanced Diagnostic and Therapeutic Care Pathways (PDTAs) based on the prediction of chronic disease using telemedicine technologies and machine learning (ML) data processing techniques. The aim is to decrease health risk and avoid hospitalizations through prevention. The proposed method defines a Process Mining Organization (PMO) model, managing risks using a PDTA structured to prevent chronic risk. Specifically, the data analysis is focused on stroke risk. First, we applied and compared the Random Forest (RF) and Gradient Boosted Trees (GBT) supervised algorithms to predict stroke risk, and then, the Fuzzy c-Means unsupervised algorithm to cluster information on the predicted results. The application of the proposed approach is able to increase the efficiency of healthcare human resources and drastically decrease care costs.https://www.mdpi.com/2673-4117/5/1/15prevention of chronic diseaseprocess miningProcess Mining Organization (PMO)machine learningdecision makingtelemedicine |
| spellingShingle | Angelo Rosa Alessandro Massaro Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic Diseases Eng prevention of chronic disease process mining Process Mining Organization (PMO) machine learning decision making telemedicine |
| title | Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic Diseases |
| title_full | Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic Diseases |
| title_fullStr | Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic Diseases |
| title_full_unstemmed | Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic Diseases |
| title_short | Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic Diseases |
| title_sort | process mining organization pmo based on machine learning decision making for prevention of chronic diseases |
| topic | prevention of chronic disease process mining Process Mining Organization (PMO) machine learning decision making telemedicine |
| url | https://www.mdpi.com/2673-4117/5/1/15 |
| work_keys_str_mv | AT angelorosa processminingorganizationpmobasedonmachinelearningdecisionmakingforpreventionofchronicdiseases AT alessandromassaro processminingorganizationpmobasedonmachinelearningdecisionmakingforpreventionofchronicdiseases |