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Predicting Diabetic Patient Hospital Readmission Using Optimized Random Forest and Firefly Evolutionary Algorithm

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@article{IJASEIT14221,
   author = {Nida Aslam and Irfan Ullah Khan and Samar Alkhalifah and Sarah Abbas AL-Sadiq and Shahad Wael Bughararah and Meznah Abdullah AL-Otabi and Zainab Mohammed AL-Odinie},
   title = {Predicting Diabetic Patient Hospital Readmission Using Optimized Random Forest and Firefly Evolutionary Algorithm},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {5},
   year = {2021},
   pages = {1876--1883},
   keywords = {Diabetes patient’s hospital readmission; optimized random forest; firefly technique; bio-inspired; SMOTE.},
   abstract = {Diabetes is one of the most prevailing diseases worldwide. The number of hospitalized patients with diabetes is usually huge. Readmission in the hospital is expensive, and early prediction of diabetes patient’s hospital readmission can reduce the cost and help healthcare professionals evaluate the quality of healthcare services at the hospital. The proposed study aimed to develop an early prediction model for diabetes readmission and identify the significant factors that lead to readmission of diabetes patients. The early prediction will reduce the risk of hospital readmission. Several machine learning classifiers, such as Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF), were applied. Firefly bio-inspired technique was used for feature selection and model optimization. Synthetic Minority Oversampling Technique (SMOTE) was applied to alleviate the data imbalance problem. The performance of the classifiers was compared using different feature sets. Experiments showed that RF outperformed the other models using reduced features selected by the Firefly algorithm. The study achieved the highest accuracy, precision, recall, and Area Under Curve (AUC) of 0.99, 0.99, 0.94, and 0.98, respectively. The results show the significance of the proposed model in diabetes readmission prediction. As a result, it is suggested that other system models and multiple data sets be investigated in order to obtain better results and identify significant features for early readmission prediction in diabetic patients.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14221},
   doi = {10.18517/ijaseit.11.5.14221}
}

EndNote

%A Aslam, Nida
%A Khan, Irfan Ullah
%A Alkhalifah, Samar
%A AL-Sadiq, Sarah Abbas
%A Bughararah, Shahad Wael
%A AL-Otabi, Meznah Abdullah
%A AL-Odinie, Zainab Mohammed
%D 2021
%T Predicting Diabetic Patient Hospital Readmission Using Optimized Random Forest and Firefly Evolutionary Algorithm
%B 2021
%9 Diabetes patient’s hospital readmission; optimized random forest; firefly technique; bio-inspired; SMOTE.
%! Predicting Diabetic Patient Hospital Readmission Using Optimized Random Forest and Firefly Evolutionary Algorithm
%K Diabetes patient’s hospital readmission; optimized random forest; firefly technique; bio-inspired; SMOTE.
%X Diabetes is one of the most prevailing diseases worldwide. The number of hospitalized patients with diabetes is usually huge. Readmission in the hospital is expensive, and early prediction of diabetes patient’s hospital readmission can reduce the cost and help healthcare professionals evaluate the quality of healthcare services at the hospital. The proposed study aimed to develop an early prediction model for diabetes readmission and identify the significant factors that lead to readmission of diabetes patients. The early prediction will reduce the risk of hospital readmission. Several machine learning classifiers, such as Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF), were applied. Firefly bio-inspired technique was used for feature selection and model optimization. Synthetic Minority Oversampling Technique (SMOTE) was applied to alleviate the data imbalance problem. The performance of the classifiers was compared using different feature sets. Experiments showed that RF outperformed the other models using reduced features selected by the Firefly algorithm. The study achieved the highest accuracy, precision, recall, and Area Under Curve (AUC) of 0.99, 0.99, 0.94, and 0.98, respectively. The results show the significance of the proposed model in diabetes readmission prediction. As a result, it is suggested that other system models and multiple data sets be investigated in order to obtain better results and identify significant features for early readmission prediction in diabetic patients.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14221
%R doi:10.18517/ijaseit.11.5.14221
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 5
%@ 2088-5334

IEEE

Nida Aslam,Irfan Ullah Khan,Samar Alkhalifah,Sarah Abbas AL-Sadiq,Shahad Wael Bughararah,Meznah Abdullah AL-Otabi and Zainab Mohammed AL-Odinie,"Predicting Diabetic Patient Hospital Readmission Using Optimized Random Forest and Firefly Evolutionary Algorithm," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 5, pp. 1876-1883, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.5.14221.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Aslam, Nida
AU  - Khan, Irfan Ullah
AU  - Alkhalifah, Samar
AU  - AL-Sadiq, Sarah Abbas
AU  - Bughararah, Shahad Wael
AU  - AL-Otabi, Meznah Abdullah
AU  - AL-Odinie, Zainab Mohammed
PY  - 2021
TI  - Predicting Diabetic Patient Hospital Readmission Using Optimized Random Forest and Firefly Evolutionary Algorithm
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 5
Y2  - 2021
SP  - 1876
EP  - 1883
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Diabetes patient’s hospital readmission; optimized random forest; firefly technique; bio-inspired; SMOTE.
N2  - Diabetes is one of the most prevailing diseases worldwide. The number of hospitalized patients with diabetes is usually huge. Readmission in the hospital is expensive, and early prediction of diabetes patient’s hospital readmission can reduce the cost and help healthcare professionals evaluate the quality of healthcare services at the hospital. The proposed study aimed to develop an early prediction model for diabetes readmission and identify the significant factors that lead to readmission of diabetes patients. The early prediction will reduce the risk of hospital readmission. Several machine learning classifiers, such as Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF), were applied. Firefly bio-inspired technique was used for feature selection and model optimization. Synthetic Minority Oversampling Technique (SMOTE) was applied to alleviate the data imbalance problem. The performance of the classifiers was compared using different feature sets. Experiments showed that RF outperformed the other models using reduced features selected by the Firefly algorithm. The study achieved the highest accuracy, precision, recall, and Area Under Curve (AUC) of 0.99, 0.99, 0.94, and 0.98, respectively. The results show the significance of the proposed model in diabetes readmission prediction. As a result, it is suggested that other system models and multiple data sets be investigated in order to obtain better results and identify significant features for early readmission prediction in diabetic patients.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14221
DO  - 10.18517/ijaseit.11.5.14221

RefWorks

RT Journal Article
ID 14221
A1 Aslam, Nida
A1 Khan, Irfan Ullah
A1 Alkhalifah, Samar
A1 AL-Sadiq, Sarah Abbas
A1 Bughararah, Shahad Wael
A1 AL-Otabi, Meznah Abdullah
A1 AL-Odinie, Zainab Mohammed
T1 Predicting Diabetic Patient Hospital Readmission Using Optimized Random Forest and Firefly Evolutionary Algorithm
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 5
YR 2021
SP 1876
OP 1883
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Diabetes patient’s hospital readmission; optimized random forest; firefly technique; bio-inspired; SMOTE.
AB Diabetes is one of the most prevailing diseases worldwide. The number of hospitalized patients with diabetes is usually huge. Readmission in the hospital is expensive, and early prediction of diabetes patient’s hospital readmission can reduce the cost and help healthcare professionals evaluate the quality of healthcare services at the hospital. The proposed study aimed to develop an early prediction model for diabetes readmission and identify the significant factors that lead to readmission of diabetes patients. The early prediction will reduce the risk of hospital readmission. Several machine learning classifiers, such as Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF), were applied. Firefly bio-inspired technique was used for feature selection and model optimization. Synthetic Minority Oversampling Technique (SMOTE) was applied to alleviate the data imbalance problem. The performance of the classifiers was compared using different feature sets. Experiments showed that RF outperformed the other models using reduced features selected by the Firefly algorithm. The study achieved the highest accuracy, precision, recall, and Area Under Curve (AUC) of 0.99, 0.99, 0.94, and 0.98, respectively. The results show the significance of the proposed model in diabetes readmission prediction. As a result, it is suggested that other system models and multiple data sets be investigated in order to obtain better results and identify significant features for early readmission prediction in diabetic patients.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14221
DO  - 10.18517/ijaseit.11.5.14221