Volume 12, Issue 4 (2024)
Health Educ Health Promot 2024, 12(4): 649-660 |
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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-5-77715-en.html
URL: http://hehp.modares.ac.ir/article-5-77715-en.html
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
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
Abstract: (263 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.
Instruments & 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.
Findings: 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.
Instruments & 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.
Findings: 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.
Article Type: Original Research |
Subject:
Health Literacy
Received: 2024/10/28 | Accepted: 2024/12/1 | Published: 2024/12/12
Received: 2024/10/28 | Accepted: 2024/12/1 | Published: 2024/12/12
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