An Experimental Study of Diabetes Disease Prediction System Using Classification Techniques

Tamilvanan, B.; Bhaskaran, Dr.V. Murali

doi:10.9790/0661-1901043944

Cited by 17 publications

(2 citation statements)

References 8 publications

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“…Still, in the instance of the leaf node, it uses NB to categorize, that is, during cross-validation NB tree algorithm chooses whether to go with the NB model (for leaf node) or to split the node further in the process of constructing a DT. 34…”

Section: Naïve Bayes Treementioning

confidence: 99%

Fault detection of automobile suspension system using decision tree algorithms: A machine learning approach

Balaji

Sugumaran

2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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The study aims to detect multiple faults that are exhibited by suspension system components during prolonged usage. Faults such as strut worn out, strut external damage, strut mount fault, lower arm ball joint fault, lower arm bush worn out and tie rod ball joint fault were considered in this study. A novel approach is proposed in the present study that involves vibration signals and machine learning techniques to identify various suspension system faults. Vibration signals were acquired for different fault conditions (as mentioned above) at three different load conditions by a specially fabricated experimental setup. Statistical features were extracted from the acquired vibration signals from which the most significant features were selected using J48 decision tree algorithm. The selected features were provided as input to the tree-based family of algorithms to determine the best in class classification algorithm for suspension fault diagnosis. The results obtained enumerate that the random forest classifier produces the best classification accuracy for all the load conditions (no load, half load, and full load) with values of 95.88%, 94.88%, and 92.01%, respectively. Finally, the performance of the proposed classification model is compared with other state-of-the-art machine learning classifiers.

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Section: Naïve Bayes Treementioning

confidence: 99%

Fault detection of automobile suspension system using decision tree algorithms: A machine learning approach

Balaji

Sugumaran

2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…IDMPF is based on the principles of data analytic lifecycle [13]. The proposed IDMPF is evaluated using the decision tree (DT)-based random forest (RF) and support vector machine (SVM) classification models, as they are the most used in the literature [8,12,[14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] from 2010 to 2019, as shown in Figure S2 (https://github.com/Dr-Leila-Ismail). Very few works compare RF and SVM [19,20,22].…”

Section: Introductionmentioning

confidence: 99%

IDMPF: intelligent diabetes mellitus prediction framework using machine learning

Ismail

Materwala

2021

ACI

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PurposeMachine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.Design/methodology/approachHealth professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.FindingsThe authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.Originality/valueThis paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.

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Prediction of Diabetes Using Ensemble Learning Model

Singh

Gupta

2021

Advances in Intelligent Systems and Computing

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An Experimental Study of Diabetes Disease Prediction System Using Classification Techniques

Cited by 17 publications

References 8 publications

Fault detection of automobile suspension system using decision tree algorithms: A machine learning approach

Fault detection of automobile suspension system using decision tree algorithms: A machine learning approach

IDMPF: intelligent diabetes mellitus prediction framework using machine learning

Prediction of Diabetes Using Ensemble Learning Model

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