Intelligent Prediction of Human Health Risks Based on Medical History: A Review
The main access to modern healthcare using artificial technologies is related to the medical topic and prediction of risks to human health. Enhance patient medical care using intelligent prediction models such as machine learning, like gradient boosting trees, supervised machine learning and logisti...
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| Format: | Article |
| Language: | English |
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Mosul University
2024-12-01
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| Series: | Al-Rafidain Journal of Computer Sciences and Mathematics |
| Subjects: | |
| Online Access: | https://csmj.uomosul.edu.iq/article_185888_49ac3d843f3254fe06e46973165fa9af.pdf |
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| author | Mais Irreem kamal Fawziya Ramo |
| author_facet | Mais Irreem kamal Fawziya Ramo |
| author_sort | Mais Irreem kamal |
| collection | DOAJ |
| description | The main access to modern healthcare using artificial technologies is related to the medical topic and prediction of risks to human health. Enhance patient medical care using intelligent prediction models such as machine learning, like gradient boosting trees, supervised machine learning and logistic regression which have a great importance in detecting diseases by analyzing medical images and diagnosing chronic diseases, in addition to use deep learning models like deep neural networks, recurrent neural networks, and long short term memory to predict many disease like depression risk, lung cancer, heart diabetic and kidney diseases. Enhance healthcare provider insights using intelligent prediction models to predict future health conditions, treatment outcomes, and disease progression. Moreover, the contribution of the intelligent prediction model helps the healthcare professional identify potential risks and intervene proactively by analyzing patient historical data for early diseases detection like using. Ultimately, the combination of medical history, intelligent prediction, and healthcare data analysis will empower healthcare providers with valuable tools to improve patient outcomes in efficient healthcare organizations. |
| format | Article |
| id | doaj-art-b4e4496a045d430791205b5397384e0e |
| institution | Kabale University |
| issn | 1815-4816 2311-7990 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Mosul University |
| record_format | Article |
| series | Al-Rafidain Journal of Computer Sciences and Mathematics |
| spelling | doaj-art-b4e4496a045d430791205b5397384e0e2025-08-20T03:39:22ZengMosul UniversityAl-Rafidain Journal of Computer Sciences and Mathematics1815-48162311-79902024-12-01182334510.33899/csmj.2024.147703.1113185888Intelligent Prediction of Human Health Risks Based on Medical History: A ReviewMais Irreem kamal0Fawziya Ramo1Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, IraqDepartment of Computer Science, College of Computer Science and Mathematics, University of Mosul, IraqThe main access to modern healthcare using artificial technologies is related to the medical topic and prediction of risks to human health. Enhance patient medical care using intelligent prediction models such as machine learning, like gradient boosting trees, supervised machine learning and logistic regression which have a great importance in detecting diseases by analyzing medical images and diagnosing chronic diseases, in addition to use deep learning models like deep neural networks, recurrent neural networks, and long short term memory to predict many disease like depression risk, lung cancer, heart diabetic and kidney diseases. Enhance healthcare provider insights using intelligent prediction models to predict future health conditions, treatment outcomes, and disease progression. Moreover, the contribution of the intelligent prediction model helps the healthcare professional identify potential risks and intervene proactively by analyzing patient historical data for early diseases detection like using. Ultimately, the combination of medical history, intelligent prediction, and healthcare data analysis will empower healthcare providers with valuable tools to improve patient outcomes in efficient healthcare organizations.https://csmj.uomosul.edu.iq/article_185888_49ac3d843f3254fe06e46973165fa9af.pdfmachine learningdeep learninghealth carepredictionmedical history |
| spellingShingle | Mais Irreem kamal Fawziya Ramo Intelligent Prediction of Human Health Risks Based on Medical History: A Review Al-Rafidain Journal of Computer Sciences and Mathematics machine learning deep learning health care prediction medical history |
| title | Intelligent Prediction of Human Health Risks Based on Medical History: A Review |
| title_full | Intelligent Prediction of Human Health Risks Based on Medical History: A Review |
| title_fullStr | Intelligent Prediction of Human Health Risks Based on Medical History: A Review |
| title_full_unstemmed | Intelligent Prediction of Human Health Risks Based on Medical History: A Review |
| title_short | Intelligent Prediction of Human Health Risks Based on Medical History: A Review |
| title_sort | intelligent prediction of human health risks based on medical history a review |
| topic | machine learning deep learning health care prediction medical history |
| url | https://csmj.uomosul.edu.iq/article_185888_49ac3d843f3254fe06e46973165fa9af.pdf |
| work_keys_str_mv | AT maisirreemkamal intelligentpredictionofhumanhealthrisksbasedonmedicalhistoryareview AT fawziyaramo intelligentpredictionofhumanhealthrisksbasedonmedicalhistoryareview |