Ontology-Based Machine Learning to Predict Diabetes Patients

Massari, Hakim El; Mhammedi, Sajida; Sabouri, Zineb; Gherabi, Noreddine

doi:10.1007/978-3-030-91738-8_40

Cited by 12 publications

(7 citation statements)

References 14 publications

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“…The approach used to classify the dataset using the ontology model was published and detailed in our previous work [2], we recommend reading it for more details. Here, we will give some details briefly.…”

Section: Ontology Modelmentioning

confidence: 99%

“…It is important in computer science because of its capacity to represent diverse concepts and their relationships in different disciplines. In actuality, no single ontology is sufficient to follow the growing demands of today's healthcare, and the ontologies must be integrated with algorithms of machine learning to support data integration and analysis [2]. In previous work, we already created and explored an ontologybased model capable of predicting diabetes patients by using an ontology classifier based on a decision tree algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Diabetes Prediction Using Machine Learning Algorithms and Ontology

Massari

Sabouri

Mhammedi

et al. 2022

JICTS

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Diabetes is one of the chronic diseases, which is increasing from year to year. The problems begin when diabetes is not detected at an early phase and diagnosed properly at the appropriate time. Different machine learning techniques, as well as ontology-based ML techniques, have recently played an important role in medical science by developing an automated system that can detect diabetes patients. This paper provides a comparative study and review of the most popular machine learning techniques and ontology-based Machine Learning classification. Various types of classification algorithms were considered namely: SVM, KNN, ANN, Naive Bayes, Logistic regression, and Decision Tree. The results are evaluated based on performance metrics like Recall, Accuracy, Precision, and F-Measure that are derived from the confusion matrix. The experimental results showed that the best accuracy goes for ontology classifiers and SVM.

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Section: Ontology Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diabetes Prediction Using Machine Learning Algorithms and Ontology

Massari

Sabouri

Mhammedi

et al. 2022

JICTS

Self Cite

View full text Add to dashboard Cite

show abstract

“…The method used to classify the dataset using the ontology model was previously published and discussed in this earlier study [2], which we recommend reading for more information. We'll go through some specifics shortly here.…”

Section: Ontology Modelmentioning

confidence: 99%

The Impact of Ontology on the Prediction of Cardiovascular Disease Compared to Machine Learning Algorithms

et al. 2022

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Cardiovascular disease is one of the chronic diseases that is on the rise. The complications occur when cardiovascular disease is not discovered early and correctly diagnosed at the right time. Various machine learning approaches, including ontology-based Machine Learning techniques, have lately played an essential role in medical science by building an automated system that can identify heart illness. This paper compares and reviews the most prominent machine learning algorithms, as well as ontology-based Machine Learning classification. Random Forest, Logistic regression, Decision Tree, Naive Bayes, k-Nearest Neighbours, Artificial Neural Network, and Support Vector Machine were among the classification methods explored. The dataset used consists of 70000 instances and can be downloaded from the Kaggle website. The findings are assessed using performance measures generated from the confusion matrix, such as F-Measure, Accuracy, Recall, and Precision. The results showed that the ontology outperformed all the machine learning algorithms.

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“…Linear discriminant analysis was selected by [6] with an accuracy of 77 as the best model versus the other used machine learning techniques. Artificial neural networks, ontology classifiers, K-nearest neighbors, support vector machine, Naive Bayes, decision tree, and logistic regression were utilized to classify diabetes [9]. The ontology classifier with an accuracy of 77.5 was nominated as the best classification model.…”

Section: Introductionmentioning

confidence: 99%

Diabetes type 2 classification using machine learning algorithms with up-sampling technique

Saeed

2023

Journal of Electrical Systems and Inf Technol

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Recently, the rate of chronic diabetes disease has increased extensively. Diabetes increases blood sugar and other problems like blurred vision, kidney failure, nerve problems, and stroke. Researchers for predicting diabetes have constructed various models. In this paper, gradient boosting classifier, AdaBoost classifier, decision tree classifier, and extra trees classifier machine learning models have been utilized for identifying chronic diabetes disease. The models analyze the PIMA Indian Diabetes dataset (PIMA) and Behavioral Risk Factor Surveillance System (BRFSS) diabetes datasets to classify patients with positive or negative diagnoses. 80% of the datasets are used as training data and 20% as testing data. The extra trees classifier with an area under curve of 0.96% for PIMA and 0.99% for BRFSS datasets outperformed other models. Therefore, it is suggested that healthcare providers can use the ETC model to predict chronic disease.

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Ontology-Based Machine Learning to Predict Diabetes Patients

Cited by 12 publications

References 14 publications

Diabetes Prediction Using Machine Learning Algorithms and Ontology

Diabetes Prediction Using Machine Learning Algorithms and Ontology

The Impact of Ontology on the Prediction of Cardiovascular Disease Compared to Machine Learning Algorithms

Diabetes type 2 classification using machine learning algorithms with up-sampling technique

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