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Volume 12, Issue 4 (2024)
Health Educ Health Promot 2024, 12(4): 649-660 |
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History
Received: 2024/10/28 | Accepted: 2024/12/1 | Published: 2024/12/12
Received: 2024/10/28 | Accepted: 2024/12/1 | Published: 2024/12/12
How to cite this article
Ayaad O, Ibrahim R, AlBaimani K, AlGhaithi M, Sawaya Z, AlHasni N, et al . Predicting and Classifying the Perceptions of Learning Needs Importance in Cancer Patients; a Machine Learning Approach. Health Educ Health Promot 2024; 12 (4) :649-660
URL: http://hehp.modares.ac.ir/article-4-77715-en.html
URL: http://hehp.modares.ac.ir/article-4-77715-en.html
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Authors
O. Ayaad *1 
, R. Ibrahim1 , Kh. AlBaimani2 , M.M. AlGhaithi1 , Z.G. Sawaya3 , N.S. AlHasni3 , H.S. AlAwaisi3 , A.S. AlFahdi4 , B. Al Faliti1 , B.M. Salman5
, R. Ibrahim1 , Kh. AlBaimani2 , M.M. AlGhaithi1 , Z.G. Sawaya3 , N.S. AlHasni3 , H.S. AlAwaisi3 , A.S. AlFahdi4 , B. Al Faliti1 , B.M. Salman5
1- Quality and Accreditation Department, Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), University Medical City, Muscat, Oman
2- Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), Muscat, Oman
3- Nursing Department, Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), University Medical City, Muscat, Oman
4- Holistic Care Department, Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), University Medical City, Muscat, Oman
5- Pharmacy Department, National Hematology and Bone Marrow Transplant Center, University Medical City, Muscat, Oman
2- Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), Muscat, Oman
3- Nursing Department, Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), University Medical City, Muscat, Oman
4- Holistic Care Department, Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), University Medical City, Muscat, Oman
5- Pharmacy Department, National Hematology and Bone Marrow Transplant Center, University Medical City, Muscat, Oman
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Introduction
Machine learning (ML) and artificial intelligence (AI) are at the forefront of technological advancements in healthcare, playing pivotal roles in predicting learning needs and evaluating their perceived importance [1, 2]. These technologies leverage the power of data to create predictive models that highlight gaps in knowledge and skills, thereby empowering educators and healthcare managers to develop more effective, patient-centered learning programs. By analyzing large and complex datasets, ML algorithms can identify trends and insights that would otherwise remain hidden, helping healthcare providers and patients address specific deficiencies in knowledge and skill development [1, 2].
One of the key advantages of ML models is their ability to monitor training outcomes in real time. Unlike traditional methods of education evaluation, which may rely on periodic assessments, ML enables continuous adaptation of learning programs. For instance, when healthcare staff or patients demonstrate improvement in certain areas but show deficits in others, ML can dynamically adjust the educational content to target these emerging gaps [3]. Innovations such as federated learning have further enhanced this capability by allowing collaboration across multiple organizations while maintaining stringent data privacy standards. This ensures that learning needs are identified and prioritized on a larger scale without compromising sensitive information [3, 4]
Machine learning (ML) and artificial intelligence (AI) are at the forefront of technological advancements in healthcare, playing pivotal roles in predicting learning needs and evaluating their perceived importance [1, 2]. These technologies leverage the power of data to create predictive models that highlight gaps in knowledge and skills, thereby empowering educators and healthcare managers to develop more effective, patient-centered learning programs. By analyzing large and complex datasets, ML algorithms can identify trends and insights that would otherwise remain hidden, helping healthcare providers and patients address specific deficiencies in knowledge and skill development [1, 2].
One of the key advantages of ML models is their ability to monitor training outcomes in real time. Unlike traditional methods of education evaluation, which may rely on periodic assessments, ML enables continuous adaptation of learning programs. For instance, when healthcare staff or patients demonstrate improvement in certain areas but show deficits in others, ML can dynamically adjust the educational content to target these emerging gaps [3]. Innovations such as federated learning have further enhanced this capability by allowing collaboration across multiple organizations while maintaining stringent data privacy standards. This ensures that learning needs are identified and prioritized on a larger scale without compromising sensitive information [3, 4]