Machine Learning-Based Application for Predicting Risk of Type 2 Diabetes Mellitus (T2DM) in Saudi Arabia: A Retrospective Cross-Sectional Study

Syed, Asif Hassan; Khan, Tabrej

doi:10.1109/access.2020.3035026

Cited by 52 publications

(32 citation statements)

References 62 publications

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“…RF uses a set of homogenous decision trees as its base classifiers while the EMV classifier was composed of the three simple learners LR, SVM, and DT, and used hard voting that considered the majority for predicting the class label for each instance in the test set. The rationale for choosing these is based on their previous performance reports in similar situations [9,31,32]. As our objective was to understand the factors contributing to the classification, we chose not to use any neural networks-based classifier in our analysis due to their "black-box" nature of interpretation of the model [34,57].…”

Section: Selection Of Machine Learning Classifiersmentioning

confidence: 99%

“…These changes have led to an increasing rate of chronic diseases. Many studies conducted to address the rapid growth of Diabetes Mellitus have either the objective to quantify the status of diabetics in the country [3,5], identifying the most frequently performed self-care behaviors [6], identifying factors related to diabetes control [7], or apply mathematical [8] or machine learning models for diabetes prediction [9]. All these efforts are related to the increasing demand to enhance healthcare quality and control the elevated growth rate of diabetes in the kingdom.…”

Section: Introductionmentioning

confidence: 99%

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Investigating Health-Related Features and Their Impact on the Prediction of Diabetes Using Machine Learning

et al. 2021

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Diabetes Mellitus (DM) is one of the most common chronic diseases leading to severe health complications that may cause death. The disease influences individuals, community, and the government due to the continuous monitoring, lifelong commitment, and the cost of treatment. The World Health Organization (WHO) considers Saudi Arabia as one of the top 10 countries in diabetes prevalence across the world. Since most of its medical services are provided by the government, the cost of the treatment in terms of hospitals and clinical visits and lab tests represents a real burden due to the large scale of the disease. The ability to predict the diabetic status of a patient with only a handful of features can allow cost-effective, rapid, and widely-available screening of diabetes, thereby lessening the health and economic burden caused by diabetes alone. The goal of this paper is to investigate the prediction of diabetic patients and compare the role of HbA1c and FPG as input features. By using five different machine learning classifiers, and using feature elimination through feature permutation and hierarchical clustering, we established good performance for accuracy, precision, recall, and F1-score of the models on the dataset implying that our data or features are not bound to specific models. In addition, the consistent performance across all the evaluation metrics indicate that there was no trade-off or penalty among the evaluation metrics. Further analysis was performed on the data to identify the risk factors and their indirect impact on diabetes classification. Our analysis presented great agreement with the risk factors of diabetes and prediabetes stated by the American Diabetes Association (ADA) and other health institutions worldwide. We conclude that by performing analysis of the disease using selected features, important factors specific to the Saudi population can be identified, whose management can result in controlling the disease. We also provide some recommendations learned from this research.

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Section: Selection Of Machine Learning Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating Health-Related Features and Their Impact on the Prediction of Diabetes Using Machine Learning

et al. 2021

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“…However, Syed and Khan [57] developed utilized the Chi-Squared analysis and binary LR for analyzing and screening the most crucial diabetes uncertainty circumstance for T2DM risk prediction after utilizing the PID and NHNES datasets [57]. Furthermore, they also implemented a two-class decision forest model based on ensemble learning [57]. Their implemented model: decision forest obtained a satisfactory accuracy value of 82.1%, the precision value of 77.6%, recall value of 89%, F1-score value of 82.9%, and AUC value of 86.7%, respectively, for predicting T2DM.…”

Section: Ensemble Learning Techniquesmentioning

confidence: 98%

“…Their generated approach obtained an accuracy value of 90%, a recall value of 90.2%, and a precision value of 94.9% for predicting diabetes. However, Syed and Khan [57] developed utilized the Chi-Squared analysis and binary LR for analyzing and screening the most crucial diabetes uncertainty circumstance for T2DM risk prediction after utilizing the PID and NHNES datasets [57]. Furthermore, they also implemented a two-class decision forest model based on ensemble learning [57].…”

Section: Ensemble Learning Techniquesmentioning

confidence: 99%

Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

Dhar¹,

Ayele

2021

IEEE Access

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Various well-known health diseases affect millions of people worldwide. Sometimes in the early stage, the clinicians may not recognize several clinical symptoms due to lack of their symptoms reflection or anything else. Thus, such diseases are not easier to identify. There may have chances to grow these illnesses and affected millions of people worldwide. The risk factor of such diseases severity can be lessened, notably whenever an accurate early prediction is possible. This study presents an innovative multi-tier weighted ensemble learning model (MTWEL) for predicting several diseases such as diabetes and hepatocellular carcinoma (HCC) and, therefore, reduces such above-said problems from the sufferers and lessens the chances of mortality. In the MTWEL model, we have utilized two lists of base classifiers in which six various machine learning (ML) classifiers are assigned in each list to develop two weighted ensemble learning (EL) models and combine them to form the proposed model by employing a weighted voting approach. In the MTWEL model, the parameters of all employed classifiers are tuned through the genetic algorithm-enabled hyperparameter optimization technique to form the optimized base models. The weight of each chosen optimized base model and generated EL model(s) is calculated using Matthews correlation coefficient value with the optimized weight value. In this study, neighborhood component analysis is employed to reduce the dimension of the given input dataset. The suggested model's experimental outcomes are conducted on two real-world datasets to exhibit its performance. The suggested approach receives the best result in AUC values: 1.0 and 1.0, F1-score values: 0.9957 and 0.9947, and accuracy values: 0.9952 and 0.9929. Such outcomes in the form of performance exhibit that the proposed model is the best-suited model to predict several diseases than other techniques, and hence it helps clinicians make accurate decisions.

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“…The important advantage of diabetics care by collaboration team is to increase the efficiency of treatment of diabetics. Syed et al [12] proposed T2DM patient care. Experts evaluate the diet and begin with a retrospective evaluation.…”

Section: Literature Workmentioning

confidence: 99%

A Secure IoT-Cloud Based Healthcare System for Disease Classification Using Neural Network

Vedaraj¹,

Ezhumalai²

2022

Computer Systems Science and Engineering

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The integration of the Internet of Things (IoT) and cloud computing is the most popular growing technology in the IT world. IoT integrated cloud computing technology can be used in smart cities, health care, smart homes, environmental monitoring, etc. In recent days, IoT integrated cloud can be used in the health care system for remote patient care, emergency care, disease prediction, pharmacy management, etc. but, still, security of patient data and disease prediction accuracy is a major concern. Numerous machine learning approaches were used for effective early disease prediction. However, machine learning takes more time and less performance while classification. In this research work, the Attribute based Searchable Honey Encryption with Functional Neural Network (ABSHE-FNN) framework is proposed to analyze the disease and provide stronger security in IoT-cloud healthcare data. In this work, the Cardiovascular Disease and Pima Indians diabetes dataset are used for heart and diabetic disease classification. Initially, means-mode normalization removes the noise and normalizes the IoT data, which helps to enhance the quality of data. Rectified Linear Unit (RLU) was applied to adjust the feature weight to reduce the training cost and error classification. This proposed ABSHE-FNN technique provides better security and achieves 92.79% disease classification accuracy compared to existing techniques.

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Machine Learning-Based Application for Predicting Risk of Type 2 Diabetes Mellitus (T2DM) in Saudi Arabia: A Retrospective Cross-Sectional Study

Cited by 52 publications

References 62 publications

Investigating Health-Related Features and Their Impact on the Prediction of Diabetes Using Machine Learning

Investigating Health-Related Features and Their Impact on the Prediction of Diabetes Using Machine Learning

Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

A Secure IoT-Cloud Based Healthcare System for Disease Classification Using Neural Network

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