Rough-Fuzzy Classifier: A System to Predict the Heart Disease by Blending Two Different Set Theories

Srinivas, K.; Rao, G. Raghavendra; Govardhan, A.

doi:10.1007/s13369-013-0934-1

Cited by 33 publications

(7 citation statements)

References 6 publications

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“… Chasing behavior When a fish locates food, neighboring individuals follow. The chasing behavior is done by using equation (4).…”

Section: Searching Swarming and Chasing Operation mentioning

confidence: 99%

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A Hybrid Fish – Bee Optimization Algorithm for Heart Disease Prediction using Multiple Kernel SVM Classifier

Keerthika¹

2019

IJITEE

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The patient's heart disease status is obtained by using a heart disease detection model. That is used for the medical experts. In order to predict the heart disease, the existing technique use optimal classifier. Even though the existing technique achieved the better result, it has some disadvantages. technique is calculated based on the accuracy, sensitivity, specificity, precision, recall and F-measure. Here, for test analysis, the some datasets used i.e. Cleveland, Hungarian and Switzerland etc., that are given based on the UCI machine learning repository. The experimental outcome show that our presented technique is went better than the accuracy of 97.68%. This is for the Cleveland dataset when related with existing hybrid kernel support vector machine (HKSVM) method achieved 96.03% and optimal rough fuzzy classifier obtained 62.25%. The implementation of the proposed method is done by MATLAB platform. In order to improve those drawbacks, the suggested technique utilizes the effective method for heart disease prediction. At first the input information is preprocessed and then the preprocessed result is forwarded to the feature selection process. For the feature selection process a proficient feature selection is used over the high dimensional medical data. Hybrid Fish Bee optimization algorithm (HFSBEE) is utilized. Thus, the proposed algorithm parallelizes the two algorithms such that the local behavior of artificial bee colony algorithm and global search of fish swarm optimization are effectively used to find the optimal solution. Classification process is performed by the transformation of medical dataset to the Multi kernel support vector machine (MKSVM). The process of our proposed Rundown phrases-Artificial bee colony algorithm, Fish swarm optimization, Multi kernel support vector machine, Optimal rough fuzzy, Cleveland, Hungarian and Switzerland.

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“… Chasing behavior When a fish locates food, neighboring individuals follow. The chasing behavior is done by using equation (4).…”

Section: Searching Swarming and Chasing Operation mentioning

confidence: 99%

“…The heart muscle is blocked off when more than one coronary arteries transfer the blood to heart muscle. This is called as infarction of myocardial [4]. Transformation of blood is stopped at certain period, normally up to 20 mins the artery may die.…”

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confidence: 99%

A Hybrid Fish – Bee Optimization Algorithm for Heart Disease Prediction using Multiple Kernel SVM Classifier

Keerthika¹

2019

IJITEE

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“…The authors proposed to improve the results achieved by including other data mining techniques. Two of the same authors, furthermore, introduced a rough-fuzzy classifier that combined rough set theory with the fuzzy set with the aim of predicting heart disease [15]. Another prototype of a heart disease prediction system was proposed by Palaniappan and Awang [16] using Decision Tree, Naïve Bayes, and Neural Network classification algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Information Mining Approach for Knowledge Discovery in Cardiovascular Disease (CVD)

Pasanisi

Paiano

2018

Information

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The healthcare ambit is usually perceived as "information rich" yet "knowledge poor". Nowadays, an unprecedented effort is underway to increase the use of business intelligence techniques to solve this problem. Heart disease (HD) is a major cause of mortality in modern society. This paper analyzes the risk factors that have been identified in cardiovascular disease (CVD) surveillance systems. The Heart Care study identifies attributes related to CVD risk (gender, age, smoking habit, etc.) and other dependent variables that include a specific form of CVD (diabetes, hypertension, cardiac disease, etc.). In this paper, we combine Clustering, Association Rules, and Neural Networks for the assessment of heart-event-related risk factors, targeting the reduction of CVD risk. With the use of the K-means algorithm, significant groups of patients are found. Then, the Apriori algorithm is applied in order to understand the kinds of relations between the attributes within the dataset, first looking within the whole dataset and then refining the results through the subsets defined by the clusters. Finally, both results allow us to better define patients' characteristics in order to make predictions about CVD risk with a Multilayer Perceptron Neural Network. The results obtained with the hybrid information mining approach indicate that it is an effective strategy for knowledge discovery concerning chronic diseases, particularly for CVD risk.

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“…Different classification methods (1)(2)(3)(4)(5)(6)(7)(8)(9)(10), each with its unique advantages and disadvantages, have been presented to diagnose the CAD. While conventional methods such as decision trees (1), naive Bayes (3), etc., have some speed benefits and easily applied to data sets, these methods cannot yield significant classification performance.…”

Section: Introductionmentioning

confidence: 99%

“…While conventional methods such as decision trees (1), naive Bayes (3), etc., have some speed benefits and easily applied to data sets, these methods cannot yield significant classification performance. Therefore, machine learning based classification methods, such as neural network classifiers and fuzzy classifiers (6,8,11), have been applied in recent years to classify the CAD data to improve the classification performance.…”

Section: Introductionmentioning

confidence: 99%

Classification of coronary artery disease data sets by using a deep neural network

Çalışkan

Yüksel

2017

The EuroBiotech Journal

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In this study, a deep neural network classifier is proposed for the classification of coronary artery disease medical data sets. The proposed classifier is tested on reference CAD data sets from the literature and also compared with popular representative classification methods regarding its classification performance. Experimental results show that the deep neural network classifier offers much better accuracy, sensitivity and specificity rates when compared with other methods. The proposed method presents itself as an easily accessible and cost-effective alternative to currently existing methods used for the diagnosis of CAD and it can be applied for easily checking whether a given subject under examination has at least one occluded coronary artery or not.

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Rough-Fuzzy Classifier: A System to Predict the Heart Disease by Blending Two Different Set Theories

Cited by 33 publications

References 6 publications

A Hybrid Fish – Bee Optimization Algorithm for Heart Disease Prediction using Multiple Kernel SVM Classifier

A Hybrid Fish – Bee Optimization Algorithm for Heart Disease Prediction using Multiple Kernel SVM Classifier

A Hybrid Information Mining Approach for Knowledge Discovery in Cardiovascular Disease (CVD)

Classification of coronary artery disease data sets by using a deep neural network

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