Detecting major disease in public hospital using ensemble techniques

Firdaus, Mgs. Afriyan; Nadia, Rin; Tama, Bayu Adhi

doi:10.1109/istmet.2014.6936496

Cited by 9 publications

(5 citation statements)

References 20 publications

(10 reference statements)

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“…Artificial intelligence (AI) and machine learning techniques have brought a new magnitude to CHD detection and diagnosis. They have been employed for discovering and uncovering valuable pattern/information from the clinical datasets with a few user inputs and attempts [8,9].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…This condition leads to a significant improvement necessity of disease prediction using advanced ML techniques. For this reason, ML techniques have grown as a well-known tool for discovering and characterizing complex patterns and relationships among them from large datasets [5]. The prediction task could be either a classification or regression techniques, relying on the type of the target variable, i.e., categorical or numerical.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems

Tama

Lim

2020

Mathematics

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Classification algorithms are widely taken into account for clinical decision support systems. However, it is not always straightforward to understand the behavior of such algorithms on a multiple disease prediction task. When a new classifier is introduced, we, in most cases, will ask ourselves whether the classifier performs well on a particular clinical dataset or not. The decision to utilize classifiers mostly relies upon the type of data and classification task, thus making it often made arbitrarily. In this study, a comparative evaluation of a wide-array classifier pertaining to six different families, i.e., tree, ensemble, neural, probability, discriminant, and rule-based classifiers are dealt with. A number of real-world publicly datasets ranging from different diseases are taken into account in the experiment in order to demonstrate the generalizability of the classifiers in multiple disease prediction. A total of 25 classifiers, 14 datasets, and three different resampling techniques are explored. This study reveals that the classifier that is likely to become the best performer is the conditional inference tree forest (cforest), followed by linear discriminant analysis, generalize linear model, random forest, and Gaussian process classifier. This work contributes to existing literature regarding a thorough benchmark of classification algorithms for multiple diseases prediction.

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“…Among the diseases that affect the world population Hepatitis is found to be a global population. The World Health Organization (WHO) estimates that more than 240 million people worldwide have chronic infections of Hepatitis which leads to 600 thousand deaths every year [7]. A wide variety of techniques have also been used for the detection of Hepatitis, which are variations of decision tree and neural networks, using a given number of features; the accuracy of results using a number of features is shown in [7].…”

Section: Introductionmentioning

confidence: 99%

“…The World Health Organization (WHO) estimates that more than 240 million people worldwide have chronic infections of Hepatitis which leads to 600 thousand deaths every year [7]. A wide variety of techniques have also been used for the detection of Hepatitis, which are variations of decision tree and neural networks, using a given number of features; the accuracy of results using a number of features is shown in [7]. A biosensor is used in [8] to detect the Hepatitis virus (HBV, HAV, HCV) immobilizing specific antibodies in the sensor element.…”

Section: Introductionmentioning

confidence: 99%

Hepatitis diagnosis using optimized KD-Trees and Neural Networks

Santos-Bustos¹,

Espitia²

2020

International Journal of Engineering Research and Technology

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This article uses two computational techniques to detect Hepatitis, an inflammatory disease that affects the liver. An evaluation of techniques to detect it is presented, including KD-Trees and Neural Networks. The results show that KD-Trees system had a 59% of success while the neural network system had a 72% of success rate. Also, an analysis was carried out for each result employing receiver operating characteristic curves.

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Detecting major disease in public hospital using ensemble techniques

Cited by 9 publications

References 20 publications

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

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

A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems

Hepatitis diagnosis using optimized KD-Trees and Neural Networks

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