Amended fused TOPSIS-VIKOR for classification (ATOVIC) applied to some UCI data sets

Baccour, Leila

doi:10.1016/j.eswa.2018.01.025

Cited by 77 publications

(35 citation statements)

References 17 publications

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“…There is ample related work in the fields directly related to this paper. ANN has been introduced to produce the highest accuracy prediction in the medical field [6]. The back propagation multilayer perception (MLP) of ANN is used to predict heart disease.…”

Section: Related Workmentioning

confidence: 99%

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Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

2019

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Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

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Section: Related Workmentioning

confidence: 99%

“…Their evaluation becomes very important. We generate results using a Artificial Neural Network ANN, which produces good performance in the prediction of heart disease [6], [18]. Neural network methods are introduced, which combine not only posterior probabilities but also predicted values from multiple predecessor techniques.…”

Section: Introductionmentioning

confidence: 99%

Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

2019

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“…Even in the advanced countries one of the major reason of deaths is CVD [8]. In medical field artificial neural network (ANN) has been popularized to produce maximum accuracy [9]. The research conferred in [10] used the similar heart disease data as this study but divergent ML algorithms were enforced.…”

Section: Review Of Relevant Workmentioning

confidence: 99%

Enhanced Accuracy of Heart Disease Prediction using Machine Learning and Recurrent Neural Networks Ensemble Majority Voting Method

Javid¹,

Alsaedi²,

Ghazali³

2020

IJACSA

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To solve many problems in data science, Machine Learning (ML) techniques implicates artificial intelligence which are commonly used. The major utilization of ML is to predict the conclusion established on the extant data. Using an established dataset machine determine emulate and spread them to an unfamiliar data sets to anticipate the conclusion. A few classification algorithm's accuracy prediction is satisfactory, although other perform limited accuracy. Different ML and Deep Learning (DL) networks established on ANN have been extensively recommended for the disclosure of heart disease in antecedent researches. In this paper, we used UCI Heart Disease dataset to test ML techniques along with conventional methods (i.e. random forest, support vector machine, K-nearest neighbor), as well as deep learning models (i.e. long short-term-memory and gated-recurrent unit neural networks). To improve the accuracy of weak algorithms we explore voting based model by combining multiple classifiers. A provisional cogent approach was used to regulate how the ensemble technique can be enforced to improve an accuracy in the heart disease prediction. The strength of the proposed ensemble approach such as voting based model is compelling in improving the prognosis accuracy of anemic classifiers and established adequate achievement in analyze risk of heart disease. A superlative increase of 2.1% accuracy for anemic classifiers was attained with the help of an ensemble voting based model.

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“…TOPSIS and VIKOR methods are effective and practical tools for solving many MCGDM problems. Given their same principle and individual strong points, a novel method is developed combining TOPSIS method and VIKOR method called TOPSIS–VIKOR method (Baccour, ).…”

Section: Mcgdm Approach Based On Distance Of Pltssmentioning

confidence: 99%

Distance‐based multicriteria group decision‐making approach with probabilistic linguistic term sets

Wang

Zhang

2018

Expert Systems

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Probabilistic linguistic term sets (PLTSs) are an important expression for hesitant linguistic preference information under group decision‐making circumstances. This study investigates problems of multicriteria group decision making (MCGDM) with PLTSs. A novel and rational comparison method is first proposed, and two distance measures for PLTSs are defined. The weight of each criterion is then obtained via maximum deviation method. Subsequently, extended Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS) ‐ VIseKriterijumska Optimizacija I Kompromisno Resenje a Serbian name (VIKOR) and TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) methods are developed as decision support models to handle MCGDM problems. An illustrative example is also analysed to demonstrate the rationality and feasibility of the proposed methods.

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Amended fused TOPSIS-VIKOR for classification (ATOVIC) applied to some UCI data sets

Cited by 77 publications

References 17 publications

Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

Enhanced Accuracy of Heart Disease Prediction using Machine Learning and Recurrent Neural Networks Ensemble Majority Voting Method

Distance‐based multicriteria group decision‐making approach with probabilistic linguistic term sets

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