Identification of heart disease using fuzzy neural genetic algorithm with data mining techniques

Meda, Srikanth; Bhogapathi, Raveendra Babu

doi:10.18280/ama_b.610208

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…They have shown different algorithm techniques with complete information like used database and accuracy. Meda-Bhogapathi [17] developed a heart attack risk assessment model using genetic algorithms and data mining techniques. The author has used the UCI database for testing purposes and provided a comparison table of previous work and techniques.…”

Section: Related Work Discussionmentioning

confidence: 99%

“…Cardiovascular disease occurs when the heart functionality is not running smoothly, and due to this, the heart does not pump the required amount of blood to other parts of the body, causing heart failure. It is sometimes challenging to identify heart disease due to several factors like blood pressure, cholesterol, heart rate, and chest pain type [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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FRBF: A Fuzzy Rule Based Framework for Heart Disease Diagnosis

Tanmay¹

2022

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Heart disease is also known as cardiovascular disease. It is one of the most dangerousand deadly disease in all over the globe. Cardiovascular disease was deemed as amajor illness in old and middle age, but recent trends shown that now cardiovasculardisease is also a deadly disease in young age group due to irregular habit. However,Angiography is one of the way to diagnose heart disease, but it is very expensive andalso has major side effect. The aim of this research paper is to design a fuzzy rulebased framework to diagnosis of the risk level of the heart disease. Our proposedframework used a Mamdani interface system and used UCI machine repositorydataset for heart disease diagnosis. In this proposed study, we have used 10 Inputattribute and one output attribute with 554 rules. Besides, a comparative table is alsopresented, where proposed methodology is better than other methodology. Accordingto the proposed methodology results, that the performance is highly successful and itis a promising tool for identification of a heart disease patient at an early stage. Wehave achieved accuracy, sensitivity rates of 95.2% and 87.04 respectively, on the UCIdataset.

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Section: Related Work Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FRBF: A Fuzzy Rule Based Framework for Heart Disease Diagnosis

Tanmay¹

2022

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“…Every layer consists of processing units, which are named neurons. The learning in the NN occurs by changing the weights of the processing units' connections after presenting each segment of data to the network, in accordance to the error estimate occurred in the output [18]. The error value in the output node j for the nth training example is calculated as in Eq.…”

Section: Classificationmentioning

confidence: 99%

Classification of heart disease using multiple classifiers

Alfonse¹

2019

RCES

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Heart disease is amongst the most widely recognized diseases in the world. This research aims to consolidate the precision of heart disease classification/diagnosis by developing a system depending on multiple classifiers. The proposed system contains two phases, which are the preprocessing phase and the classification phase. The preprocessing phase includes data cleaning, normalization and accounting for missing values. In the classification phase, multiple classifiers are used as an ensemble technique based on the Multilayer Perceptron (MLP), K-Nearest Neighbor (K-NN) and C4.5. A heart disease dataset, which contains four databases and gathered from the UCI machine learning repository, was used for experiments. The proposed classification system gives 99.4% classification precision according to 10-fold cross-validation technique. The outcome obtained from the proposed system shows that its performance is better than that of already reported classification systems.

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Identification of heart disease using fuzzy neural genetic algorithm with data mining techniques

Cited by 2 publications

References 14 publications

FRBF: A Fuzzy Rule Based Framework for Heart Disease Diagnosis

FRBF: A Fuzzy Rule Based Framework for Heart Disease Diagnosis

Classification of heart disease using multiple classifiers

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