Comparative Study of Different Classification Techniques: Heart Disease Use Case

Bouali, Hanen; Akaichi, Jalel

doi:10.1109/icmla.2014.84

Cited by 36 publications

(16 citation statements)

References 6 publications

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“…The test data points are then mapped into that same space and are classified based on which side of the margin they fall. [5].In [9], SVM performs the best with 85.7655% of correctly classified instance and in [10] SVM is used with boosting technique to give an accuracy of 84.81%. HoudaMezrigui et al have used SVM to attain a f-measure value of 93.5617 [11].…”

Section: B Support Vector Machinementioning

confidence: 99%

“…These two steps are performed recursively with the remaining attributes. In [10]decision tree has the worst performance with an accuracy of 77.55% but when decision tree is used with boosting technique it performs better with an accuracy of 82.17%.In [9] decision tree performs very poorly with a correctly classified instance percentage of 42.8954% whereas in [16] also uses the same dataset but used the J48 algorithm for implementing Decision Trees and the accuracy thus obtained is 67.7% which is less but still an improvement on the former. Renu Chauhan et al have obtained an accuracy of 71.43% [17].…”

Section: Decision Treementioning

confidence: 99%

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Heart disease prediction using machine learning techniques : a survey

Ramalingam¹,

Dandapath²,

Raja³

2018

IJET

246

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Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

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Section: B Support Vector Machinementioning

confidence: 99%

Section: Decision Treementioning

confidence: 99%

Heart disease prediction using machine learning techniques : a survey

Ramalingam¹,

Dandapath²,

Raja³

2018

IJET

246

View full text Add to dashboard Cite

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“…Also, Deng et al [4] have explored a dynamical ECG recognition framework for human identification and cardiovascular diseases classification based on radial basis function (RBF). Besides, Bouali and Akaichi [6] have used many machine learning techniques, such as: Baysian Network, Decision tree, Artificial Neural Network, Fuzzy pattern tree and Support Vector Machine (SVM), to classify the Cleavland heart disease dataset using 10-fold-cross validation. SVM achieved the highest prediction accuracy compared to other classifiers.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood Component Analysis and Support Vector Machines for Heart Disease Prediction

Djerioui¹,

Brik²,

Ladjal³

et al. 2019

ISI

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Nowadays, one of the main reasons for disability and mortality premature in the world is the heart disease, which make its prediction is a critical challenge in the area of healthcare systems. In this paper, we propose a heart disease prediction system based on Neighborhood Component Analysis (NCA) and Support Vector Machine (SVM). In fact, NCA is used for selecting the most relevant parameters to make a good decision. This can seriously reduce the time, materials, and labor to get the final decision while increasing the prediction performance. Besides, the binary SVM is used for predicting the selected parameters in order to identify the presence/absence of heart disease. The conducted experiments on real heart disease dataset show that the proposed system achieved 85.43% of prediction accuracy. This performance is 1.99% higher than the accuracy obtained with the whole parameters. Also, the proposed system outperforms the state-of-the-art heart disease prediction.

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“…These data often contain hidden patterns and relationships which can lead to improved diagnosis and treatment, and provides a platform to better understand the mechanisms governing almost all aspects of the medical domain [21]. Various data mining techniques, namely, decision tree [22][23][24][25][26][27][28], support vector machine (SVM) [24,25,27], artificial neural networks (ANN) [24,25,27,28], Naïve Bayes [28], Bayesian Networks [25], have been used for CVD diagnosis as black box and models generated were not clinically interpretable. On the other hand, the rules generated by decision trees are clinically interpretable, which is highly desirable in clinical applications [29].…”

Section: Introductionmentioning

confidence: 99%

An intelligent noninvasive model for coronary artery disease detection

Verma

Srivastava

Negi

2017

Complex Intell. Syst.

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Coronary artery disease (CAD) is one of the leading causes of death globally. Angiography is one of the benchmarked diagnoses for detection of CAD; however, it is costly, invasive, and requires a high level of technical expertise. This paper discusses a data mining technique that uses noninvasive clinical data to identify CAD cases. The clinical data of 335 subjects were collected at the cardiology department, Indira Gandhi Medical College, Shimla, India, over the period of 2012-2013. Only 48.9% subjects showed coronary stenosis in coronary angiography and were confirmed cases of CAD. A large number of cases (171 out of 335) were found normal after invasive diagnosis. Hence, a requirement of noninvasive technique was felt that could identify CAD cases without going for invasive diagnosis. We applied data mining classification techniques on noninvasive clinical data. The data set is analyzed using a hybrid and novel k-means cluster centroid-based method for missing value imputation and C4.5, NB Tree and multilayer perceptron for modeling to predict CAD patients. The proposed hybrid method increases the accuracy achieved by the basic techniques of classification. This framework is a promising tool for screening CAD and its severity with high probability and low cost.

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Comparative Study of Different Classification Techniques: Heart Disease Use Case

Cited by 36 publications

References 6 publications

Heart disease prediction using machine learning techniques : a survey

Heart disease prediction using machine learning techniques : a survey

Neighborhood Component Analysis and Support Vector Machines for Heart Disease Prediction

An intelligent noninvasive model for coronary artery disease detection

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