Efficient Deep Learning Models for Predicting Super-Utilizers in Smart Hospitals

Jaffar, M. Arfan; Shafiq, Sundas; Shahzadi, Nazia; Alrajeh, Nabil; Jamil, Mohsin; Javaid, Nadeem

doi:10.1109/access.2023.3305379

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…These models have performed exceptionally well with small sequences however their performance declines with longer sequences [19] and [49] . Moreover, LSTMs can circumvent the vanishing gradient problem [50] , yet they are sensitive to the exploding gradient issue. In textual data analysis, LSTMs like other RNNs, give higher weights to words in closer proximities and the upstream context is emphasized higher than the downstream context.…”

Section: Proposed System Modelmentioning

confidence: 99%

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

Latif,

Javaid,

Aslam

et al. 2024

Heliyon

Self Cite

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Section: Proposed System Modelmentioning

confidence: 99%

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

Latif,

Javaid,

Aslam

et al. 2024

Heliyon

Self Cite

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“…Professionals have recently enhanced their use of computer technology to improve decision-making support. The diagnosis of patients using Machine Learning (ML) is becoming increasingly important in the healthcare industry [2].…”

Section: Introductionmentioning

confidence: 99%

Heart Disease Prediction Using Stacking Model With Balancing Techniques and Dimensionality Reduction

Noor,

Javaid,

Alrajeh

et al. 2023

IEEE Access

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Heart disease is a serious worldwide health issue with wide-reaching effects. Since heart disease is one of the leading causes of mortality worldwide, early detection is crucial. Emerging technologies like Machine Learning (ML) are currently being actively used by the biomedical, healthcare, and health prediction industries. PaRSEL, a new stacking model is proposed in this research, that combines four classifiers, Passive Aggressive Classifier (PAC), Ridge Classifier (RC), Stochastic Gradient Descent Classifier (SGDC), and eXtreme Gradient Boosting (XGBoost), at the base layer, and LogitBoost is deployed for the final predictions at the meta layer. The imbalanced and irrelevant features in the data increase the complexity of the classification models. The dimensionality reduction and data balancing approaches are considered very important for lowering costs and increasing the accuracy of the model. In PaRSEL, three dimensionality reduction techniques, Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and Factor Analysis (FA), are used to reduce the dimensionality and select the most relevant features for the diagnosis of heart disease. Furthermore, eight balancing techniques, Proximity Weighted Random Affine Shadowsampling (ProWRAS), Localized Randomized Affine Shadowsampling (LoRAS), Random Over Sampling (ROS), Adaptive Synthetic (ADASYN), Synthetic Minority Oversampling Technique (SMOTE), Borderline SMOTE (B-SMOTE), Majority Weighted Minority Oversampling Technique (MWMOTE) and Random Walk Oversampling (RWOS), are used to deal with the imbalanced nature of the dataset. The performance of PaRSEL is compared with the other standalone classifiers using different performance measures like accuracy, F1-score, precision, recall and AUC-ROC score. Our proposed model achieves 97% accuracy, 80% F1-score, precision is greater than 90%, 67% recall, and 98% AUC-ROC score. This shows that PaRSEL outperforms other standalone classifiers in terms of heart disease prediction. Additionally, we deploy SHapley Additive exPlanations (SHAP) on our proposed model. It helps to understand the internal working of the model. It illustrates how much influence a classifier has on the final prediction outcome.

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Heart Disease Prediction Using Novel Ensemble and Blending Based Cardiovascular Disease Detection Networks: EnsCVDD-Net and BlCVDD-Net

Khan,

Javaid,

Bashir

et al. 2024

IEEE Access

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Efficient Deep Learning Models for Predicting Super-Utilizers in Smart Hospitals

Cited by 6 publications

References 33 publications

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

Heart Disease Prediction Using Stacking Model With Balancing Techniques and Dimensionality Reduction

Heart Disease Prediction Using Novel Ensemble and Blending Based Cardiovascular Disease Detection Networks: EnsCVDD-Net and BlCVDD-Net

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