Comparative Analysis of Diabetes Mellitus Predictive Machine Learning Classifiers

Samet, Sarra; Laouar, Mohamed Ridda; Bendib, Issam

doi:10.1007/978-3-031-25344-7_27

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…In Samet (2023), a comparative analysis is conducted among different classifiers on a dataset for detecting and predicting diabetes mellitus. Random forest achieves the highest accuracy of 93 percent among the supervised learning techniques studied.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel ensemble machine learning framework for early stage diabetes mellitus prediction

Nagpal,

Sabharwal,

Tripathi

2023

Multidiscip. Sci. J.

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Diabetes Mellitus is a harmful condition characterized by elevated blood sugar levels resulting from insufficient insulin production in the body. This disorder gives rise to various health complications affecting the kidneys, heart, nerves, and eyes. If left unidentified and untreated, it can even prove fatal. Therefore, early detection and prediction of chronic diseases are imperative, and advancements in technology have led to a shift towards personalized healthcare. Machine learning offers a viable solution for predicting the likelihood of developing the disease with satisfactory accuracy.The objective of this research is to create a machine learning-based model for accurately predicting early-stage diabetes mellitus. The study employs PCA for dimensionality reduction and the ensemble bagging decision tree classification, thereby, achieving a remarkable accuracy level concerning the disease. The proposed model is evaluated using a publicly available dataset of 520 instances. The dataset utilized in this research includes information such as Polyuria, Polydipsia, sudden_weight_loss and weakness etc. The model achieves an impressive accuracy of 93.26%, precision of 96.9%, recall of 94.1%, and F-Score of 95.4%. Comparative analysis against other existing techniques demonstrates its superior performance in predicting such chronic conditions.

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Section: Literature Reviewmentioning

confidence: 99%

A novel ensemble machine learning framework for early stage diabetes mellitus prediction

Nagpal,

Sabharwal,

Tripathi

2023

Multidiscip. Sci. J.

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“…Machine learning algorithms analyze a dataset consisting of features and, based on observations of prelabeled data, are aimed at classifying new data into predefined classes. Random forest, naive Bayes, and artificial neural networks are among the algorithms commonly used to generate diabetes detection models [12][13][14], among others. Another crucial aspect in generating classification models is the dataset employed.…”

Section: Introductionmentioning

confidence: 99%

Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation

García-Domínguez

Galván-Tejada

Magallanes‐Quintanar

et al. 2023

Journal of Diabetes Research

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The development of medical diagnostic models to support healthcare professionals has witnessed remarkable growth in recent years. Among the prevalent health conditions affecting the global population, diabetes stands out as a significant concern. In the domain of diabetes diagnosis, machine learning algorithms have been widely explored for generating disease detection models, leveraging diverse datasets primarily derived from clinical studies. The performance of these models heavily relies on the selection of the classifier algorithm and the quality of the dataset. Therefore, optimizing the input data by selecting relevant features becomes essential for accurate classification. This research presents a comprehensive investigation into diabetes detection models by integrating two feature selection techniques: the Akaike information criterion and genetic algorithms. These techniques are combined with six prominent classifier algorithms, including support vector machine, random forest, k-nearest neighbor, gradient boosting, extra trees, and naive Bayes. By leveraging clinical and paraclinical features, the generated models are evaluated and compared to existing approaches. The results demonstrate superior performance, surpassing accuracies of 94%. Furthermore, the use of feature selection techniques allows for working with a reduced dataset. The significance of feature selection is underscored in this study, showcasing its pivotal role in enhancing the performance of diabetes detection models. By judiciously selecting relevant features, this approach contributes to the advancement of medical diagnostic capabilities and empowers healthcare professionals in making informed decisions regarding diabetes diagnosis and treatment.

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Building Risk Prediction Models for Diabetes Decision Support System

Samet

Laouar

2023

Lecture Notes in Business Information Processing

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Comparative Analysis of Diabetes Mellitus Predictive Machine Learning Classifiers

Cited by 3 publications

References 18 publications

A novel ensemble machine learning framework for early stage diabetes mellitus prediction

A novel ensemble machine learning framework for early stage diabetes mellitus prediction

Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation

Building Risk Prediction Models for Diabetes Decision Support System

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