Decision making model to predict presence of coronary artery disease using neural network and C5.0 decision tree

Ahmadi, Ehsan; Weckman, Gary R.; Masel, Dale T.

doi:10.1007/s12652-017-0499-z

Cited by 27 publications

(11 citation statements)

References 24 publications

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“…First, the attributes generated in the dimensional reduction process, when referring to the costing groupings of Feshki and Shijani's [2] research results, there are two costly checks, namely scintigraphy and fluoroscopy. A similar result is also made by Ahmadi et.al [3], a combination model of C5.0 with a neural network, which does not reduce both the costly attributes. Second, dimensional reduction process does not take into account the clinical procedures that clinicians normally perform.…”

Section: Introductionsupporting

confidence: 79%

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A Tiered Approach On Dimensional Reduction Process for Prediction of Coronary Heart Disease

Wiharto

Herianto

Kusnanto

2018

IJEECS

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<p>The use of dimensional reduction in the diagnostic system model of coronary heart disease, many same of case do not take into account the clinical procedures commonly used by clinicians in diagnosis. This requires that the examination be done thoroughly, thus making the high cost of diagnosis. This study aims to develop a tiered approach model in reducing dimensions for predicting CHD. The method in this research is divided into several stages, namely preprocessing, building the knowledge base and system testing. Preprocessing consists of several processes, namely the removal of missing value data, grouping attributes, and dividing data for training and testing. Knowledge base modeling is divided into three levels. The first level were the risk factor attributes, the second level were the type of chest pain & ECG, and the third were scintigraphy & coronary angiography. The knowledge base was modeled based on fuzzy rules and its inferencing process using Mamdani method. The first, fuzzy rule-based was obtained by using the FRS study. The second and third stage, using the induction rule algorithm to get the rule, then converted to fuzzy rule. The tested algorithm were C4.5, CART, and FDT. The system testing was performed by the 5-folds cross-validation method, with performance parameters based on population and individual. The test resulted using the Cleveland and Hungarian datasets, the FRS+CART combination was capable of reducing the most attributes and the highest likelihood ratio performance parameter, which was 15.96. FRS+C4.5, at least the attributes were reduced, but has an AUC performance of 80.43%, while FRS+FDT, more reduced attributes than FRS+C4.5, and AUC performance parameters are better than FRS+CART. Dimensional reduction model for prediction of CHD, capable of providing better performance than not tiered.<em></em></p>

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Section: Introductionsupporting

confidence: 79%

“…Referring to the likelihood ratio, it can then be used to calculate the pre-test and post-test probability. The performance parameters can be formulated in the Equation (1)(2)(3)(4)(5)(6)(7)(8). pre − test odds = pre − test probability: (1 − pre − test probability)…”

Section: Methodsmentioning

confidence: 99%

A Tiered Approach On Dimensional Reduction Process for Prediction of Coronary Heart Disease

Wiharto

Herianto

Kusnanto

2018

IJEECS

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“…Logistic regression was reported as the best classifier, providing 85% accuracy on the Statlog dataset. Furthermore, the performance of boosted C5.0 and NN were compared to predict CHD for the Cleveland dataset [24]. Based on the experiment, the authors concluded that there was no significant difference between C5.0 and NN.…”

Section: Related Workmentioning

confidence: 99%

Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble

Tama

Lee

2020

BioMed Research International

114

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Coronary heart disease (CHD) is one of the severe health issues and is one of the most common types of heart diseases. It is the most frequent cause of mortality across the globe due to the lack of a healthy lifestyle. Owing to the fact that a heart attack occurs without any apparent symptoms, an intelligent detection method is inescapable. In this article, a new CHD detection method based on a machine learning technique, e.g., classifier ensembles, is dealt with. A two-tier ensemble is built, where some ensemble classifiers are exploited as base classifiers of another ensemble. A stacked architecture is designed to blend the class label prediction of three ensemble learners, i.e., random forest, gradient boosting machine, and extreme gradient boosting. The detection model is evaluated on multiple heart disease datasets, i.e., Z-Alizadeh Sani, Statlog, Cleveland, and Hungarian, corroborating the generalisability of the proposed model. A particle swarm optimization-based feature selection is carried out to choose the most significant feature set for each dataset. Finally, a two-fold statistical test is adopted to justify the hypothesis, demonstrating that the performance differences of classifiers do not rely upon an assumption. Our proposed method outperforms any base classifiers in the ensemble with respect to 10-fold cross validation. Our detection model has performed better than current existing models based on traditional classifier ensembles and individual classifiers in terms of accuracy, F1, and AUC. This study demonstrates that our proposed model adds a considerable contribution compared to the prior published studies in the current literature.

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“…C5.0 decision trees and rule-based models have been first advanced by R. Quinlan in 1992, under the name "C4.5", which was an extension of a previous algorithm called Iterative Dichotomizer 3 (ID3); C4.5 was later improved into the new C5.0 classifier, which has superior efficiency [77]. C5.0 decision trees are versatile, swift and easy to use, and their use for QSAR modeling is seen as a reasonable option [78].…”

Section: Classification Algorithmsmentioning

confidence: 99%

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

Ancuceanu

Hovaneț

Anghel

et al. 2020

IJMS

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Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

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Decision making model to predict presence of coronary artery disease using neural network and C5.0 decision tree

Cited by 27 publications

References 24 publications

A Tiered Approach On Dimensional Reduction Process for Prediction of Coronary Heart Disease

A Tiered Approach On Dimensional Reduction Process for Prediction of Coronary Heart Disease

Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

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