Predictions in heart disease using techniques of data mining

Gandhi, Monika; Singh, Sanjiv

doi:10.1109/ablaze.2015.7154917

Cited by 113 publications

(41 citation statements)

References 2 publications

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“…[17]. Important points relevant to various methods of information abstraction with the aid of the use of data mining methods that are being used in brand new lookup for prediction of coronary heart disease [18].…”

Section: Related Workmentioning

confidence: 99%

Cardiovascular Disease Prediction System Using Extra Trees Classifier

Shafique

Mehmood

Ullah

et al. 2019

Preprint

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Heart Disease as cardiovascular disease is the leading cause of death for both men and women. It is the major cause of morbidity and mortality in present society. Therefore, researchers are working to help health care professionals in diagnosing process by using data mining techniques. Although the health care industry is richer in the database this data is not properly mined in order to discover hidden patterns and can able to make decisions based on these patterns. The major goal of this learning refers the extraction of hidden layers by applying numerous data mining techniques that probably give remarkable results in order to ensure the presence of cardiovascular disease among peoples. Data mining classification techniques are used to discover these patterns for research in medical industry. The dataset containing 13 attributes has analyzed for prediction system. The dataset contains some commonly used medical terms like blood pressure, cholesterol level, chest pain and 11 other attributes used to predict cardiovascular disease. The most common and effective classification techniques that are used in mining process are Verdict Tree commonly known as Decision Tree, Extra Trees Classifier, Random Forest, Support Vector Machine, Naive Bays and Logistic Regression has analyzed in this paper. Diagnosing and controlling ratio of deaths from cardiovascular disease Extra classifier trees consider is the best approach. We evaluate these prediction models by using evaluation parameters which are Accuracy, Precision, Recall, and F1-score. As per our experimental results shows accuracy of Extra trees classifier, Logistic Model tree classifier, support vector machine, and naive bays classifiers are 90%, 88%, 87%, 86% respectively. So as per our experiment analysis Extra Tree classifier with highest accuracy considered best approach for predication cardiovascular disease.

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Section: Related Workmentioning

confidence: 99%

Cardiovascular Disease Prediction System Using Extra Trees Classifier

Shafique

Mehmood

Ullah

et al. 2019

Preprint

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“…In the recent past large number of researchers worked towards the identification of the disease at the early stages [10]. In [4,9,11] different machine learning techniques are explored for heart disease prediction. In [9] in addition to that a support system is created to assist medical professionals to accurately predict the disease.…”

Section: Related Workmentioning

confidence: 99%

Robust Cluster-then-label (RCTL) Approach for Heart Disease Prediction

Bodapati¹,

Sajja²

2019

ISI

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Heart attack or stroke occurs due to the narrowed or blocked blood vessels that are present in the heart. If not identified at the early stages of the disease, there is a high probability to loose life. An algorithm that takes the advantage of the inferences given by unsupervised learning is proposed. Our proposed framework leverages the unsupervised information that is hidden in the data to improve the accuracy of the classifier. By using clustering, a label is obtained and this cluster label is included as one of the features. This augmented feature set fed as input to the classifier to predict the disease. For clustering to avoid uncertainty, k-means and spectral clustering are used. If the cluster label predicted by both the methods match, then only that information is included as one of the features. To evaluate the effectiveness of the proposed algorithm two heart disease benchmark datasets, Framingham and stat-log are used. For the classification task Logistic regression, Naïve Baye's and SVM are being used to make the model simple yet effective. Based on the experimental results we claim that by augmenting the feature set with the cluster label using the proposed method significantly improves the performance of the heart disease prediction task.

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“…If the logarithmic expression is changed by the four arithmetic operations, the running time in the whole building tree process will improve rapidly. (3) It is hard to control the tree size in the process of building a decision tree. At present, most improved methods take pruning methods [35] to avoid over-fitting phenomena, which will lead to the whole process of building decision tree models finished in two steps (i.e., modeling and pruning).…”

Section: Algorithm 1: Id3 Algorithmmentioning

confidence: 99%

“…Faced with the challenge of the above issue, data mining [1,2] technologies came into being and showed strong vitality. Data mining includes several important technologies such as classification [3], clustering [4], regression [5], etc. The classification mining technology, among data mining technologies, is becoming a most active and mature research direction allowing for successful applications.…”

Section: Introductionmentioning

confidence: 99%

Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree

Wang

Song

et al. 2017

Algorithms

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Abstract:The decision tree algorithm is a core technology in data classification mining, and ID3 (Iterative Dichotomiser 3) algorithm is a famous one, which has achieved good results in the field of classification mining. Nevertheless, there exist some disadvantages of ID3 such as attributes biasing multi-values, high complexity, large scales, etc. In this paper, an improved ID3 algorithm is proposed that combines the simplified information entropy based on different weights with coordination degree in rough set theory. The traditional ID3 algorithm and the proposed one are fairly compared by using three common data samples as well as the decision tree classifiers. It is shown that the proposed algorithm has a better performance in the running time and tree structure, but not in accuracy than the ID3 algorithm, for the first two sample sets, which are small. For the third sample set that is large, the proposed algorithm improves the ID3 algorithm for all of the running time, tree structure and accuracy. The experimental results show that the proposed algorithm is effective and viable.

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Predictions in heart disease using techniques of data mining

Cited by 113 publications

References 2 publications

Cardiovascular Disease Prediction System Using Extra Trees Classifier

Cardiovascular Disease Prediction System Using Extra Trees Classifier

Robust Cluster-then-label (RCTL) Approach for Heart Disease Prediction

Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree

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