Ensemble Gain Ratio Feature Selection (EGFS) Model with Machine Learning and Data Mining Algorithms for Disease Risk Prediction

Pasha, Syed Javeed; Mohamed, Elhassan

doi:10.1109/icict48043.2020.9112406

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…In Rao & Renuka (2020) , Naive Bayes and a Decision Tree, built using the ID3 algorithm, are used to perform a binary prediction about whether the patient is affected by hypo- or hyperthyroidism. The authors of the study in Pasha & Mohamed (2020) perform feature selection on the UCI dataset for thyroid disease detection by exploiting both a Random Forest-based method and a Gain Ratio technique. In the end, the prediction is performed by comparing also different machine learning techniques, namely K-Nearest-Neighbor, Logistic Regression, and Naive Bayes.…”

Section: Resultsmentioning

confidence: 99%

“…Looking at the public datasets, we observed that the most used dataset is the UCI one ( https://archive.ics.uci.edu/ml/datasets/Thyroid+Disease ), exploited 27 times ( Duggal & Shukla, 2020 ; Shahid et al, 2019 ; Pan et al, 2016 ; Pavya & Srinivasan, 2017 ; Mahurkar & Gaikwad, 2017 ; Ahmed & Soomrani, 2016 ; Tyagi, Mehra & Saxena, 2018 ; Kumar, 2020 ; Pasha & Mohamed, 2020 ; Shen et al, 2016 ; Bentaiba-Lagrid et al, 2020 ; Raisinghani et al, 2019 ; Vivar et al, 2020 ; Li et al, 2019b ; Ma et al, 2018 ; Kour, Manhas & Sharma, 2020 ; Khan, 2021 ; Priyadharsini & Sasikala, 2022 ; Peya, Chumki & Zaman, 2021 ; Chaubey et al, 2021 ; Hosseinzadeh et al, 2021 ; Juneja, 2022 ; Kishor & Chakraborty, 2021 ; Islam et al, 2022 ; Saktheeswari & Balasubramanian, 2021 ; Chandel et al, 2016 ; Priya & Manavalan, 2018 ). The UCI dataset is characterized by 7,200 instances and 21 categorical and real attributes.…”

Section: Resultsmentioning

confidence: 99%

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A systematic review on artificial intelligence techniques for detecting thyroid diseases

Aversano,

Bernardi,

Cimitile

et al. 2023

PeerJ Computer Science

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The use of artificial intelligence approaches in health-care systems has grown rapidly over the last few years. In this context, early detection of diseases is the most common area of application. In this scenario, thyroid diseases are an example of illnesses that can be effectively faced if discovered quite early. Detecting thyroid diseases is crucial in order to treat patients effectively and promptly, by saving lives and reducing healthcare costs. This work aims at systematically reviewing and analyzing the literature on various artificial intelligence-related techniques applied to the detection and identification of various diseases related to the thyroid gland. The contributions we reviewed are classified according to different viewpoints and taxonomies in order to highlight pros and cons of the most recent research in the field. After a careful selection process, we selected and reviewed 72 papers, analyzing them according to three main research questions, i.e., which diseases of the thyroid gland are detected by different artificial intelligence techniques, which datasets are used to perform the aforementioned detection, and what types of data are used to perform the detection. The review demonstrates that the majority of the considered papers deal with supervised methods to detect hypo- and hyperthyroidism. The average accuracy of detection is high (96.84%), but the usage of private and outdated datasets with a majority of clinical data is very common. Finally, we discuss the outcomes of the systematic review, pointing out advantages, disadvantages, and future developments in the application of artificial intelligence for thyroid diseases detection.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A systematic review on artificial intelligence techniques for detecting thyroid diseases

Aversano,

Bernardi,

Cimitile

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Examples of filter approaches are Gain Ratio (GR) and Information Gain (IG). GR is a technique to assess the reliability of dimensions by measuring the gain ratio [32]. The main workflow of IG is to choose a probability to evaluate the value in the data split by calculating the gain for each dimension.…”

Section: Feature Selectionmentioning

confidence: 99%

“…The main workflow of IG is to choose a probability to evaluate the value in the data split by calculating the gain for each dimension. Any dimension with the highest gain will be selected as the discriminant subgroup [32]. In addition, the wrapper approach is a method that selects important features by calculating weights and measuring accuracy in segmentation to create a new feature set by increasing or decreasing the number of features from the original dataset [33].…”

Section: Feature Selectionmentioning

confidence: 99%

Improving the Avoidant Personality Disorder Prediction for Higher Education Using SMOTE-ENN and Multi-Layer Perceptron Neural Network

Nuanmeesri

Poomhiran

2023

TEM Journal

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Adolescents in higher education are more prone to Avoidant Personality Disorder (AVPD), which strongly affects academic achievement. The goal of this study was to create models for accurate prediction of the likelihood of Avoidant Personality Disorder among students in higher education. Information Gain, Gain Ratio, and Wrapper Approach are used as feature selection methods combined with data resampling techniques and machine learning, including Multi-Layer Perceptron Neural Network, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machine. The findings revealed that the Wrapper approach gave higher accuracy than Information Gain and Gain Ratio approach. Further, using the Gain Ratio approach gives the model a slightly higher efficiency than the Information Gain. Furthermore, when comparing feature selection and data resampling, it was found that the model using feature selection had more higher model efficiency than data resampling alone. Additionally, combining the Synthetic Minority Over-sampling Technique with Edited Nearest Neighbor (SMOTE-ENN) considerably increased the model’s effectiveness. Finally, the model’s efficiency was at its maximum, with an accuracy of 95.52%, when the Wrapper approach was used in conjunction with the Synthetic Minority Over-sampling Technique, the Edited Nearest Neighbor algorithm, and the Multi-Layer Perceptron Neural Network.

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“…The eight features subset was applied with the radial basis function (RBF) kernel-based SVM, and they achieved an accuracy of 85.82%. Javeed et al [18] designed an ensemble gain ratio feature selection (EGFS) model employing an RF as an ensemble algorithm and a gain ratio algorithm in order to extract features that aid in performance improvement. Via KNN, LR, and NB, they applied their model to medical datasets of UCI to attain an accurate disease risk prediction.…”

Section: A Feature Selection Techniques In Heart Disease Risk Predicmentioning

confidence: 99%

Novel Feature Reduction (NFR) Model With Machine Learning and Data Mining Algorithms for Effective Disease Risk Prediction

Pasha

Mohamed

2020

IEEE Access

Self Cite

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Presently, the application of machine learning (ML) and data mining (DM) techniques have a vital role in healthcare systems and wisely convert all obtainable data into beneficial knowledge. It is proven from the literature works that a chance of 12% error remains in the diagnosis of the diseases by the medical practitioners. Moreover, for effective disease risk prediction in medical analysis, more emphasis is accorded to the area under the curve (AUC) with accuracy as an evaluation metric. However, the role of the AUC has not been previously characterized notably. In this research article, a novel feature reduction (NFR) model that is aligned with the ML and DM algorithms is proposed to reduce the error rate and further improve the performance. The proposed NFR model comprises of two approaches and uses the AUC in addition to the accuracy to achieve a robust and effective disease risk prediction. The first approach is based on a heuristic process evaluating performance by reducing features with respect to the improvement in the AUC besides the accuracy as evaluation metrics, working to obtain the best subset of highly contributing features in the prediction. The second approach evaluates the accuracy and AUC of all individual features and forms the subsets with the highest accuracies, AUCs, and least difference between them, which are combined in various combinations to achieve the best-reduced set of highly relevant features. For this purpose, the benchmarked public heart datasets of the ML repository of the University of California, Irvine (UCI) are tested; the results are promising. The highest accuracy and AUC achieved with the proposed NFR model are 95.52% and 99.20% with 41.67% feature reduction, respectively. The accuracy is 4.22% higher than recent existing research with a significant improvement of 25% in the performance of the running time of the algorithm.

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Ensemble Gain Ratio Feature Selection (EGFS) Model with Machine Learning and Data Mining Algorithms for Disease Risk Prediction

Cited by 11 publications

References 12 publications

A systematic review on artificial intelligence techniques for detecting thyroid diseases

A systematic review on artificial intelligence techniques for detecting thyroid diseases

Improving the Avoidant Personality Disorder Prediction for Higher Education Using SMOTE-ENN and Multi-Layer Perceptron Neural Network

Novel Feature Reduction (NFR) Model With Machine Learning and Data Mining Algorithms for Effective Disease Risk Prediction

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