Genetic algorithm based feature selection and MOE Fuzzy classification algorithm on Pima Indians Diabetes dataset

Vaishali, R.; Sasikala, R.; Ramasubbareddy, Somula; Remya, S; Nalluri, Sravani

doi:10.1109/iccni.2017.8123815

Cited by 83 publications

(26 citation statements)

References 3 publications

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“…It requires to be rejected from data distribution as the classifiers are very much sensitive to the data range and distribution of the attributes. The mathematical formulation for the outlier rejection in this literature can be written as in (2).…”

Section: ) Preprocessingmentioning

confidence: 99%

“…GitHub: https://github.com/kamruleee51/Diabetes-Prediction-Using-MLClassifiers Data: https://www.kaggle.com/uciml/pima-indians-diabetes-database pancreatic beta cells, cardiovascular dysfunction, cerebral vascular dysfunction, peripheral vascular diseases, sexual dysfunction, joint failure, weight loss, ulcer, and pathogenic effects on immunity [2]. Research on diabetes patients demonstrates that diabetes among adults (over 18 years old) has risen from 4.7 % to 8.5 % in 1980 to 2014 respectively and rapidly growing up in second and third world countries [3].…”

Section: Introductionmentioning

confidence: 99%

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Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers

et al. 2020

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Diabetes, also known as chronic illness, is a group of metabolic diseases due to a high level of sugar in the blood over a long period. The risk factor and severity of diabetes can be reduced significantly if the precise early prediction is possible. The robust and accurate prediction of diabetes is highly challenging due to the limited number of labeled data and also the presence of outliers (or missing values) in the diabetes datasets. In this literature, we are proposing a robust framework for diabetes prediction where the outlier rejection, filling the missing values, data standardization, feature selection, K-fold cross-validation, and different Machine Learning (ML) classifiers (k-nearest Neighbour, Decision Trees, Random Forest, AdaBoost, Naive Bayes, and XGBoost) and Multilayer Perceptron (MLP) were employed. The weighted ensembling of different ML models is also proposed, in this literature, to improve the prediction of diabetes where the weights are estimated from the corresponding Area Under ROC Curve (AUC) of the ML model. AUC is chosen as the performance metric, which is then maximized during hyperparameter tuning using the grid search technique. All the experiments, in this literature, were conducted under the same experimental conditions using the Pima Indian Diabetes Dataset. From all the extensive experiments, our proposed ensembling classifier is the best performing classifier with the sensitivity, specificity, false omission rate, diagnostic odds ratio, and AUC as 0.789, 0.934, 0.092, 66.234, and 0.950 respectively which outperforms the state-of-the-art results by 2.00 % in AUC. Our proposed framework for the diabetes prediction outperforms the other methods discussed in the article. It can also provide better results on the same dataset which can lead to better performance in diabetes prediction. Our source code for diabetes prediction is made publicly available. INDEX TERMS Diabetes prediction, ensembling classifier, machine learning, multilayer perceptron, missing values and outliers, Pima Indian Diabetic dataset.

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Section: ) Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers

et al. 2020

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“…e dataset used in this study is taken from the National Institute of Diabetes and Digestive and Kidney Diseases. As discussed by [23], this is a publicly available dataset at [30]. A detailed description of all attributes is given in Table 3.…”

Section: Data Descriptionmentioning

confidence: 99%

An Enhanced Machine Learning Framework for Type 2 Diabetes Classification Using Imbalanced Data with Missing Values

et al. 2021

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Diabetes is one of the most common metabolic diseases that cause high blood sugar. Early diagnosis of such a condition is challenging due to its complex interdependence on various factors. There is a need to develop critical decision support systems to assist medical practitioners in the diagnosis process. This research proposes developing a predictive model that can achieve a high classification accuracy of type 2 diabetes. The study consisted of two fundamental parts. Firstly, the study investigated handling missing data adopting data imputation, namely, median value imputation, K-nearest neighbor imputation, and iterative imputation. Consequently, the study validated the implications of these imputations using various classification algorithms, i.e., linear, tree-based, and ensemble algorithms, to see how each method affected classification accuracy. Secondly, Artificial Neural Network was employed to model the best performing imputed data, balanced with SMOTETomek ensuring each class is represented fairly. This approach provided the best accuracy of 98% on the test data, outperforming accuracies achieved in prior studies using the same dataset. The dataset used in this study is concerned with gender and population. As a prospect, the study recommends adopting a larger population sample without geographic boundaries. Additionally, as the developed Artificial Neural Network model did not undergo any specific hyperparameter tuning, it would be interesting to explore tuning on top of normalized data to optimize accuracy further.

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“…A new multi-label feature selection method in classification for a multi-objective Particle Swarm Optimization PSO algorithm is presented in [14]. The NSGA-II and Evolutionary Non-Dominated Radial Slots based algorithm (ENORA) is reported in [15]. An algorithm to eliminate irrelevant, noisy and redundant features during face recognition is developed in [16].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Multiobjective Algorithms for the Classification of Multi-Label Video Datasets

et al. 2020

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It is of great importance to extract and validate an optimal subset of non-dominated features for effective multi-label classification. However, deciding on the best subset of features is an NP-Hard problem and plays a key role in improving the prediction accuracy and the processing time of video datasets. In this study, we propose autoencoders for dimensionality reduction of video data sets and ensemble the features extracted by the multi-objective evolutionary Non-dominated Sorting Genetic Algorithm and the autoencoder. We explore the performance of well-known multi-label classification algorithms for video datasets in terms of prediction accuracy and the number of features used. More specifically, we evaluate Non-dominated Sorting Genetic Algorithm-II, autoencoders, ensemble learning algorithms, Principal Component Analysis, Information Gain, and Correlation Based Feature Selection. Some of these algorithms use feature selection techniques to improve the accuracy of the classification. Experiments are carried out with local feature descriptors extracted from two multi-label datasets, the MIR-Flickr dataset which consists of images and the Wireless Multimedia Sensor dataset that we have generated from our video recordings. Significant improvements in the accuracy performance of the algorithms are observed while the number of features are being reduced.

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Genetic algorithm based feature selection and MOE Fuzzy classification algorithm on Pima Indians Diabetes dataset

Cited by 83 publications

References 3 publications

Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers

Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers

An Enhanced Machine Learning Framework for Type 2 Diabetes Classification Using Imbalanced Data with Missing Values

Analysis of Multiobjective Algorithms for the Classification of Multi-Label Video Datasets

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