Volume 12, Issue 4 (2024)
Health Educ Health Promot 2024, 12(4): 1001-1035 |
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Ibrahim R, AlBaimani K, Mustafa Salman B, Said AlHasni N, Gaby Sawaya Z, Mohammed AlGhaithi M, et al . Predicting and Classifying Perceptions of Learning Needs Importance among Patients with Cancer: A Machine Learning Approach. Health Educ Health Promot 2024; 12 (4) :1001-1035
URL: http://hehp.modares.ac.ir/article-5-77715-en.html
URL: http://hehp.modares.ac.ir/article-5-77715-en.html
Rawan Ibrahim1, Khalid AlBaimani2, Bushra Mustafa Salman3, Nabiha Said AlHasni4, Zeina Gaby Sawaya4, Mashan Mohammed AlGhaithi4, Amal Sulaiman AlFahdi5, Huda Shinoon AlAwaisi4, Balaqis Al Faliti1, Omar Ayaad *6
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 (KA.albamani@cccrc.gov.om)
3- Pharmacy Department, National Hematology and Bone Marrow Transplant Center, University Medical City, Muscat, Oman
4- Nursing Department, Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), University Medical City, Muscat, Oman
5- Holistic Care Department, Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), University Medical City, Muscat, Oman
6- Quality and Accreditation Department, Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), University Medical City, Muscat, Oman ,o.ayaad@cccrc.gov.om
2- Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), Muscat, Oman (KA.albamani@cccrc.gov.om)
3- Pharmacy Department, National Hematology and Bone Marrow Transplant Center, University Medical City, Muscat, Oman
4- Nursing Department, Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), University Medical City, Muscat, Oman
5- Holistic Care Department, Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), University Medical City, Muscat, Oman
6- Quality and Accreditation Department, Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), University Medical City, Muscat, Oman ,
Abstract: (113 Views)
Aims: Artificial intelligence (AI) and machine learning (ML) are revolutionizing healthcare by enhancing the prediction of learning needs and enabling tailored educational interventions for patients and staff.This study explores the application of AI and ML models to predict learning needs from the patient's perspective.
Methods: Three ML models—Linear Regression, Random Forest, and Gradient Boosting—were trained on health literacy, demographic, and treatment data from 218 cancer patients at Sultan Qaboos Comprehensive Cancer Center. Evaluation metrics included Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R² Score, and Area Under the Curve (AUC). Classification models (Random Forest, Gradient Boosting, Decision Tree, and Extra Trees) were assessed for Accuracy, Precision, Recall, F1-score, and AUC in categorizing learning needs.
Results: Gradient Boosting had the best predictive performance (MAE: 0.0534, RMSE: 0.0788, R²: 0.9844, AUC: 0.96), followed by Random Forest (AUC: 0.93). Linear Regression was less effective (AUC: 0.85). Key predictors included literacy level in chemotherapy, hormonal therapy, and treatment experiences, while demographic factors had minimal impact. For classification, Gradient Boosting and Decision Tree models achieved the highest accuracy (96.51%) and AUC (0.96). Random Forest showed 94.19% accuracy, while Extra Trees had 90.70%, indicating variability in model performance.
Conclusion: AI and ML, particularly Gradient Boosting, demonstrate strong potential in predicting and categorizing learning needs. These models enable targeted educational strategies, addressing knowledge gaps and aligning interventions with treatment experiences to improve healthcare quality.
Methods: Three ML models—Linear Regression, Random Forest, and Gradient Boosting—were trained on health literacy, demographic, and treatment data from 218 cancer patients at Sultan Qaboos Comprehensive Cancer Center. Evaluation metrics included Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R² Score, and Area Under the Curve (AUC). Classification models (Random Forest, Gradient Boosting, Decision Tree, and Extra Trees) were assessed for Accuracy, Precision, Recall, F1-score, and AUC in categorizing learning needs.
Results: Gradient Boosting had the best predictive performance (MAE: 0.0534, RMSE: 0.0788, R²: 0.9844, AUC: 0.96), followed by Random Forest (AUC: 0.93). Linear Regression was less effective (AUC: 0.85). Key predictors included literacy level in chemotherapy, hormonal therapy, and treatment experiences, while demographic factors had minimal impact. For classification, Gradient Boosting and Decision Tree models achieved the highest accuracy (96.51%) and AUC (0.96). Random Forest showed 94.19% accuracy, while Extra Trees had 90.70%, indicating variability in model performance.
Conclusion: AI and ML, particularly Gradient Boosting, demonstrate strong potential in predicting and categorizing learning needs. These models enable targeted educational strategies, addressing knowledge gaps and aligning interventions with treatment experiences to improve healthcare quality.
Keywords: Artificial intelligence, machine learning, learning needs, healthcare education, predictive modeling, health literacy, personalized interventions
Article Type: Original Research |
Subject:
Health Literacy
Received: 2024/10/28 | Accepted: 2024/12/11 | Published: 2024/10/19
Received: 2024/10/28 | Accepted: 2024/12/11 | Published: 2024/10/19
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