Classification of Pima Indian Diabetes Dataset using Ensemble of Decision Tree, Logistic Regression and Neural Network

Abedini, Mani; Bijari, Anita; Banirostam, Touraj

doi:10.17148/ijarcce.2020.9701

Cited by 19 publications

(14 citation statements)

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“…It can be concluded that a Naïve Bayes model works well with a more fine-tuned selection of features for binary classification. Abedin et al [25] studied a hierarchical ensemble model to combine two classifiers that had been trained, a decision tree and a logistic regression model, and feed the output of those models to a neural network.…”

Section: Discussionmentioning

confidence: 99%

Comparing the Linear and Quadratic Discriminant Analysis of Diabetes Disease Classification Based on Data Multicollinearity

Araveeporn

2022

International Journal of Mathematics and Mathematical Sciences

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Linear and quadratic discriminant analysis are two fundamental classification methods used in statistical learning. Moments (MM), maximum likelihood (ML), minimum volume ellipsoids (MVE), and t-distribution methods are used to estimate the parameter of independent variables on the multivariate normal distribution in order to classify binary dependent variables. The MM and ML methods are popular and effective methods that approximate the distribution parameter and use observed data. However, the MVE and t-distribution methods focus on the resampling algorithm, a reliable tool for high resistance. This paper starts by explaining the concepts of linear and quadratic discriminant analysis and then presents the four other methods used to create the decision boundary. Our simulation study generated the independent variables by setting the coefficient correlation via multivariate normal distribution or multicollinearity, often through basic logistic regression used to construct the binary dependent variable. For application to Pima Indian diabetic dataset, we expressed the classification of diabetes as the dependent variable and used a dataset of eight independent variables. This paper aimed to determine the highest average percentage of accuracy. Our results showed that the MM and ML methods successfully used large independent variables for linear discriminant analysis (LDA). However, the t-distribution method of quadratic discriminant analysis (QDA) performed better when using small independent variables.

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Section: Discussionmentioning

confidence: 99%

Comparing the Linear and Quadratic Discriminant Analysis of Diabetes Disease Classification Based on Data Multicollinearity

Araveeporn

2022

International Journal of Mathematics and Mathematical Sciences

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“…Moreover, the SVM model underperforms if the number of attributes for every data point exceeds the training samples. The combinational models for diabetes prediction using Cross-validation and Self-Organizing Maps (SOM) have achieved an accuracy of 78.4% [ 32 , 33 ]. SOM can rely on the associated weights of neurons for precise classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data

Srinivasu

Shafi

Krishna³

et al. 2022

Diagnostics

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The development of genomic technology for smart diagnosis and therapies for various diseases has lately been the most demanding area for computer-aided diagnostic and treatment research. Exponential breakthroughs in artificial intelligence and machine intelligence technologies could pave the way for identifying challenges afflicting the healthcare industry. Genomics is paving the way for predicting future illnesses, including cancer, Alzheimer’s disease, and diabetes. Machine learning advancements have expedited the pace of biomedical informatics research and inspired new branches of computational biology. Furthermore, knowing gene relationships has resulted in developing more accurate models that can effectively detect patterns in vast volumes of data, making classification models important in various domains. Recurrent Neural Network models have a memory that allows them to quickly remember knowledge from previous cycles and process genetic data. The present work focuses on type 2 diabetes prediction using gene sequences derived from genomic DNA fragments through automated feature selection and feature extraction procedures for matching gene patterns with training data. The suggested model was tested using tabular data to predict type 2 diabetes based on several parameters. The performance of neural networks incorporating Recurrent Neural Network (RNN) components, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) was tested in this research. The model’s efficiency is assessed using the evaluation metrics such as Sensitivity, Specificity, Accuracy, F1-Score, and Mathews Correlation Coefficient (MCC). The suggested technique predicted future illnesses with fair Accuracy. Furthermore, our research showed that the suggested model could be used in real-world scenarios and that input risk variables from an end-user Android application could be kept and evaluated on a secure remote server.

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“…Bihter [31] used the high performance neural network on ILPD that gave the training accuracy of 71.95% and validation accuracy as 73.28% Bihter [31]. Abedini, et al [32] put forward an ensemble model comprising artificial neural network (ANN), logistic regression (LR) and decision tree (DT) to classify the PIMA dataset. The accuracy obtained by the authors was calculated as 83.08% Abedini, et al [32].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Abedini, et al [32] put forward an ensemble model comprising artificial neural network (ANN), logistic regression (LR) and decision tree (DT) to classify the PIMA dataset. The accuracy obtained by the authors was calculated as 83.08% Abedini, et al [32]. Razali, et al [33] proposed the use of neural network and bayesian model on ILPD and got the accuracy result of 66.85% and 70.52% respectively Razali, et al prediction value 74.10% Barik [34].…”

Section: Literature Reviewmentioning

confidence: 99%

Hard Voting Meta Classifier for Disease Diagnosis using Mean Decrease in Impurity for Tree Models

Altaf

Butt²,

Zaman³

2022

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To predict and detect various diseases, machine learning techniques are increasingly being used in the field of medical science. This study puts forward a bagging meta-estimator and feed forward neural network based voting ensemble with mean decrease in impurity feature selection to classify the disease datasets. The work was carried out using the Jupyter notebook data analysis tool, and Python 3 is used as a programming language. In this study, two chronic disease datasets - Indian Liver Patient dataset and the PIMA Indians diabetes dataset are used for building and testing the proposed model. The datasets are split into training and testing data in the ratio of 70:30. The experimental results illustrate that our proposed voting ensemble has an improved performance compared to the individual base learners. We also compared the accuracy of the model before and after the application of feature reduction technique. The results revealed that the accuracy increased with the removal of unimportant features. By using the proposed ensemble model, the average MSE, bias and variance were calculated as 0.311, 0.217 and 0.094 respectively for ILPD dataset. Similarly for PIMA dataset, the average MSE, bias and variance were calculated as 0.233, 0.186 and 0.047 respectively. These statistical parameters record a low score for ensemble classifier as compared to the individual constituent classifiers.

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Classification of Pima Indian Diabetes Dataset using Ensemble of Decision Tree, Logistic Regression and Neural Network

Cited by 19 publications

References 0 publications

Comparing the Linear and Quadratic Discriminant Analysis of Diabetes Disease Classification Based on Data Multicollinearity

Comparing the Linear and Quadratic Discriminant Analysis of Diabetes Disease Classification Based on Data Multicollinearity

Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data

Hard Voting Meta Classifier for Disease Diagnosis using Mean Decrease in Impurity for Tree Models

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