The Methodology for Diabetes Complications Prediction Model

Kantawong, Krittika; Tongphet, Supan; Bhrommalee, Panu; Rachata, Napa; Pravesjit, Sakkayaphop

doi:10.1109/ectidamtncon48261.2020.9090700

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…However, decision trees can be prone to overfitting as well as being unstable since adding a new attribute may result in a totally new tree (variance). These challenges can be addressed by tuning different hyperparameters such as the depth of the tree or the number of samples allowed per branch [ 13 ]. The criteria to select the attribute to split the data in each of these nodes depend on two measurements, entropy and information gain.…”

Section: Methodsmentioning

confidence: 99%

“…In addition to predicting the presence of diabetes in patients, few existing studies have reported the use of machine learning to develop prediction models of diabetes complications. For instance, in [ 13 ], a model was built to predict some chronic diabetes complications, especially eye disease, kidney disease, coronary heart disease, and hyperlipidemia. The authors started with a dataset of 455 records.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Machine Learning Approach to Predicting Diabetes Complications

et al. 2021

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Diabetes mellitus (DM) is a chronic disease that is considered to be life-threatening. It can affect any part of the body over time, resulting in serious complications such as nephropathy, neuropathy, and retinopathy. In this work, several supervised classification algorithms were applied for building different models to predict and classify eight diabetes complications. The complications include metabolic syndrome, dyslipidemia, neuropathy, nephropathy, diabetic foot, hypertension, obesity, and retinopathy. For this study, a dataset collected by the Rashid Center for Diabetes and Research (RCDR) located in Ajman, UAE, was utilized. The dataset consists of 884 records with 79 features. Some essential preprocessing steps were applied to handle the missing values and unbalanced data problems. Furthermore, feature selection was performed to select the top five and ten features for each complication. The final number of records used to train and build the binary classifiers for each complication was as follows: 428—metabolic syndrome, 836—dyslipidemia, 223—neuropathy, 233—nephropathy, 240—diabetic foot, 586—hypertension, 498—obesity, 228—retinopathy. Repeated stratified k-fold cross-validation (with k = 10 and a total of 10 repetitions) was employed for a better estimation of the performance. Accuracy and F1-score were used to evaluate the models’ performance reaching a maximum of 97.8% and 97.7% for accuracy and F1-scores, respectively. Moreover, by comparing the performance achieved using different attributes’ sets, it was found that by using a selected number of features, we can still build adequate classifiers.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Approach to Predicting Diabetes Complications

et al. 2021

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“…The model established in 2020 by the authors in Ref. [ 27 ] to forecast hyperlipidemia, coronary heart disease, kidney disease, and eye disease as potential outcomes for people with diabetes is one such example. This research made use of a dataset consisting of 455.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Diabetes Using Feature Selection and Hybrid Al-Biruni Earth Radius and Dipper Throated Optimization

et al. 2023

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Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. Methodology: In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. Results: The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. Conclusions: The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods.

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