HAP-COVID: Hybrid Model Integrating Machine Learning and Neutrosophic Statistics to Estimate Mortality Risk in COVID-19 Comorbid Patients
This study aims to utilize a hybrid predictive model to evaluate the mortality risk in COVID-19 patients with comorbidities. It is challenging to make clinical decisions while dealing with SARS-CoV-2 because of the high death rate related to chronic conditions such as diabetes, hypertension, chronic...
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| Format: | Article |
| Language: | English |
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University of New Mexico
2025-07-01
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| Series: | Neutrosophic Sets and Systems |
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| Online Access: | https://fs.unm.edu/NSS/43.HAPCOVIDHybrid.pdf |
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| _version_ | 1849340426179313664 |
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| author | Daniel Iturburu-Salvador Lorenzo Cevallos-Torres Rosangela Caicedo-Quiroz Irma Naranjo-Peña Rosa Hernández-Magallanes Rosa Gonzáles-Quiñones |
| author_facet | Daniel Iturburu-Salvador Lorenzo Cevallos-Torres Rosangela Caicedo-Quiroz Irma Naranjo-Peña Rosa Hernández-Magallanes Rosa Gonzáles-Quiñones |
| author_sort | Daniel Iturburu-Salvador |
| collection | DOAJ |
| description | This study aims to utilize a hybrid predictive model to evaluate the mortality risk in COVID-19 patients with comorbidities. It is challenging to make clinical decisions while dealing with SARS-CoV-2 because of the high death rate related to chronic conditions such as diabetes, hypertension, chronic obstructive pulmonary disease (COPD), renal failure, heart disease, and cancer. To overcome this obstacle, a Monte Carlo algorithmbased simulated database was created, with clinical variables represented by Bernoulli and truncated normal distributions. Several prediction models, including Decision Trees, Naive Bayes, and Neutrosophic Statistics, were trained using this database. While the neutrosophic model permitted risk categorization according to truth, falsity, and indeterminacy, the decision tree model outperformed the Naive Bayes model in terms of accuracy. When it came to controlling diagnostic uncertainty, the hybrid approach worked well. Ultimately, this technology provides significant assistance in intricate clinical settings, enhancing the process of medical decision-making. |
| format | Article |
| id | doaj-art-d630d68f9bcf4246b1bf39f4645f746d |
| institution | Kabale University |
| issn | 2331-6055 2331-608X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | University of New Mexico |
| record_format | Article |
| series | Neutrosophic Sets and Systems |
| spelling | doaj-art-d630d68f9bcf4246b1bf39f4645f746d2025-08-20T03:43:55ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2025-07-018958960310.5281/zenodo.16748496HAP-COVID: Hybrid Model Integrating Machine Learning and Neutrosophic Statistics to Estimate Mortality Risk in COVID-19 Comorbid PatientsDaniel Iturburu-SalvadorLorenzo Cevallos-TorresRosangela Caicedo-QuirozIrma Naranjo-PeñaRosa Hernández-MagallanesRosa Gonzáles-QuiñonesThis study aims to utilize a hybrid predictive model to evaluate the mortality risk in COVID-19 patients with comorbidities. It is challenging to make clinical decisions while dealing with SARS-CoV-2 because of the high death rate related to chronic conditions such as diabetes, hypertension, chronic obstructive pulmonary disease (COPD), renal failure, heart disease, and cancer. To overcome this obstacle, a Monte Carlo algorithmbased simulated database was created, with clinical variables represented by Bernoulli and truncated normal distributions. Several prediction models, including Decision Trees, Naive Bayes, and Neutrosophic Statistics, were trained using this database. While the neutrosophic model permitted risk categorization according to truth, falsity, and indeterminacy, the decision tree model outperformed the Naive Bayes model in terms of accuracy. When it came to controlling diagnostic uncertainty, the hybrid approach worked well. Ultimately, this technology provides significant assistance in intricate clinical settings, enhancing the process of medical decision-making.https://fs.unm.edu/NSS/43.HAPCOVIDHybrid.pdfcovid-19chronic diseasesmachine learningneutrosophic statisticshybrid predictive modelmonte carlo algorithm. |
| spellingShingle | Daniel Iturburu-Salvador Lorenzo Cevallos-Torres Rosangela Caicedo-Quiroz Irma Naranjo-Peña Rosa Hernández-Magallanes Rosa Gonzáles-Quiñones HAP-COVID: Hybrid Model Integrating Machine Learning and Neutrosophic Statistics to Estimate Mortality Risk in COVID-19 Comorbid Patients Neutrosophic Sets and Systems covid-19 chronic diseases machine learning neutrosophic statistics hybrid predictive model monte carlo algorithm. |
| title | HAP-COVID: Hybrid Model Integrating Machine Learning and Neutrosophic Statistics to Estimate Mortality Risk in COVID-19 Comorbid Patients |
| title_full | HAP-COVID: Hybrid Model Integrating Machine Learning and Neutrosophic Statistics to Estimate Mortality Risk in COVID-19 Comorbid Patients |
| title_fullStr | HAP-COVID: Hybrid Model Integrating Machine Learning and Neutrosophic Statistics to Estimate Mortality Risk in COVID-19 Comorbid Patients |
| title_full_unstemmed | HAP-COVID: Hybrid Model Integrating Machine Learning and Neutrosophic Statistics to Estimate Mortality Risk in COVID-19 Comorbid Patients |
| title_short | HAP-COVID: Hybrid Model Integrating Machine Learning and Neutrosophic Statistics to Estimate Mortality Risk in COVID-19 Comorbid Patients |
| title_sort | hap covid hybrid model integrating machine learning and neutrosophic statistics to estimate mortality risk in covid 19 comorbid patients |
| topic | covid-19 chronic diseases machine learning neutrosophic statistics hybrid predictive model monte carlo algorithm. |
| url | https://fs.unm.edu/NSS/43.HAPCOVIDHybrid.pdf |
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