A Survey of Different Approaches of Machine Learning in Healthcare Management System

Goyal, Krishan Kumar; Paray, Aejaz Hassan

doi:10.35444/ijana.2019.11032

Cited by 4 publications

(2 citation statements)

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“…This section comprehensively details the retrospective study conducted as a first step on each of the target questions for SCD patient data through descriptive statistics [6]. The study aimed to understand the determinant factors associated with SCD patients who are getting admitted and being treated at different departments of the hospital.…”

Section: Retrospective Analysismentioning

confidence: 99%

An Artificial Intelligence Approach for Data Modelling Patients Inheritance of Sickle Cell Disease (SCD) in the Eastern Regions of Saudi Arabia

Gollapalli¹,

Alfaleh²

2022

MMEP

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Sickle cell disease (SCD) is a genetic illness that affects red blood cells and can lead to major complications like Acute Chest Syndrome (ACS), Cerebrovascular Accident (CVA), and even death from a stroke. SCD is frequent in some places of the world, especially where consanguineous marriages are common, such as in Saudi Arabia. This research presents artificial intelligence (AI) models that were tested on hospital clinical data collected between 2008 to 2020 using five different classifiers: Naive Bayes, Neural Networks (NN), Support Vector Machine (SVM), J48, and PART. To select the optimum classification approach, we analyzed the models based on the accuracy kappa statistics and the classification time. We also compared performance criteria such as sensitivity, specificity, accuracy, F1 measure, and AUC. The naive Bayes classifier outperformed the other classifiers with 92.22% accuracy in our investigation, which was then utilized to determine key elements that are common between SCD patients' inheritance and demographic data. The findings of this study through AI are aimed to assist hospital doctors and practitioners understand the correlation between disease inheritance and other factors, allowing them to better manage the disease by increasing disease awareness in the community, particularly among mothers. In terms of accuracy, all classifiers obtained above 92%. J48, NN and SVM exhibited 92.99% accuracy, then PART exhibits accuracy as 92.92% followed by Naï ve Bayes at 92.22%. In terms of classification time, Naï ve Bayes was fastest (0.01 sec), then J48 (0.03 sec), then PART (0.24 sec) and SVM (0.28 sec) while NN was slowest at 22.43 sec.

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Section: Retrospective Analysismentioning

confidence: 99%

An Artificial Intelligence Approach for Data Modelling Patients Inheritance of Sickle Cell Disease (SCD) in the Eastern Regions of Saudi Arabia

Gollapalli¹,

Alfaleh²

2022

MMEP

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“…It improves the accuracy and precision by learning the hidden patterns in the training data [10]- [15]. AI trains the DL models based on advanced mathematical formulation and identifies the patterns in the available data [1], [16], [17].…”

Section: Introductionmentioning

confidence: 99%

New Hybrid Deep Learning Models to Predict Cost From Healthcare Providers in Smart Hospitals

Bhatti,

Javaid,

Mansoor

et al. 2023

IEEE Access

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Accurate cost prediction of healthcare resources is challenging as diverse factors affect the overall prediction. The cost of healthcare providers is increasing exponentially as different healthcare providers charge differently for the same service due to various factors, majorly the sky rocketing inflation and increased population. It increases the importance of predicting healthcare costs to avoid unpleasant surprises. This study aims to provide the expected cost of healthcare providers that helps the patients in resource allocation and strengthens decision-making according to their resources. This paper proposes three hybrid Deep Learning (DL) models, Visual Geometry Group and Stacked Autoencoder (VGG-SAE), Visual Geometry Group and Deep Neural Network (VGG-DNN), and Stacked Autoencoder and Deep Neural Network (SAE-DNN), which optimize learning the hidden patterns from the given data more efficiently than individual models. The three hybrid DL models estimate the cost of healthcare providers effectively. The preprocessing is performed using the mode imputation for handling the missing values, Z-score for removing the outliers and standard scaler for standardizing the data. To train the hybrid models on optimum parameters, the Random Search technique is used that provides the best hyper-parameters of each hybrid model. The interpretation of the hybrid models' output is achieved using the SHapley Additive ExPlanations (SHAP) technique. The performances of VGG-SAE, VGG-DNN, and SAE-DNN are compared with the baseline DL models such as SAE, DNN, and VGG. To assess the robustness of the proposed approach, the hybrid models are trained on two different datasets of healthcare such as Healthcare Providers and Hospital Inpatient Cost Transparency. With the hyper-parameter tuning of the Healthcare Providers Dataset, VGG-SAE achieved MSE of 0.01, RMSE of 0.13, MAE of 0.02, and R-squared of 0.98. VGG-DNN achieved MSE of 0.01, RMSE of 0.12, MAE of 0.02, and R-squared of 0.99. SAE-DNN achieved MSE of 0.01, RMSE of 0.11, MAE of 0.02, and R-squared of 0.99. With the hyper-parameter tuning of the Hospital Inpatient Cost Transparency Dataset, VGG-SAE achieved MSE of 0.007, RMSE of 0.08, MAE of 0.03, R-squared of 0.99, and execution time of 1680 seconds. VGG-DNN achieved MSE of 0.0006, RMSE of 0.08, MAE of 0.03, R-squared of 0.99, and execution time of 645 seconds. SAE-DNN achieved MSE of 0.003, RMSE of 0.06, MAE of 0.02, R-squared of 0.99, and execution time of 850 seconds. Our proposed hybrid combinations outperformed other deep models and Machine Learning (ML) techniques such as SAE, DNN, VGG, SVR and GBR, which ensures high efficiency of the proposed models in terms of healthcare providers cost.

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Applications of Machine Learning in Healthcare: An Overview

Samarpita

Satpathy

2022

2022 1st IEEE International Conference on Industrial Electronics: Developments &Amp; Applications (ICIDeA)

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A Survey of Different Approaches of Machine Learning in Healthcare Management System

Cited by 4 publications

References 0 publications

An Artificial Intelligence Approach for Data Modelling Patients Inheritance of Sickle Cell Disease (SCD) in the Eastern Regions of Saudi Arabia

An Artificial Intelligence Approach for Data Modelling Patients Inheritance of Sickle Cell Disease (SCD) in the Eastern Regions of Saudi Arabia

New Hybrid Deep Learning Models to Predict Cost From Healthcare Providers in Smart Hospitals

Applications of Machine Learning in Healthcare: An Overview

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