Computational intelligence for heart disease diagnosis: A medical knowledge driven approach

Nahar, Jesmin; Imam, Tasadduq; Tickle, Kevin S.; Chen, Yi‐Ping Phoebe

doi:10.1016/j.eswa.2012.07.032

Cited by 221 publications

(124 citation statements)

References 27 publications

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“…The work by Nahar J. et al (2013) [2] gives a knowledge driven approach. Initially Logistic Regression was used by Dr. Robert Detrano to obtain 77% accuracy (Detrano, 1989 [3]).…”

Section: Past Researchmentioning

confidence: 99%

Comparative Study of Classification Techniques (SVM, Logistic Regression and Neural Networks) to Predict the Prevalence of Heart Disease

Khanna¹,

Sahu²,

Baths³

et al. 2015

IJMLC

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Abstract-This paper does a comparative study of commonly used machine learning algorithms in predicting the prevalence of heart diseases. It uses the publicly available Cleveland Dataset and models the classification techniques on it. It brings up the differences between different models and evaluates their accuracies in predicting a heart disease. We have shown that lesser complex models such as logistic regression and support vector machines with linear kernel give more accurate results than their more complex counterparts. We have used F1 score and ROC curves as evaluative measures. Through this effort, we aim to provide a benchmark and improve earlier ones in the field of heart disease diagnostics using machine learning classification techniques.Index Terms -Cleveland heart disease dataset, classification, svm, neural networks.

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“…The work by Nahar J. et al (2013) [2] gives a knowledge driven approach. Initially Logistic Regression was used by Dr. Robert Detrano to obtain 77% accuracy (Detrano, 1989 [3]).…”

Section: Past Researchmentioning

confidence: 99%

Comparative Study of Classification Techniques (SVM, Logistic Regression and Neural Networks) to Predict the Prevalence of Heart Disease

Khanna¹,

Sahu²,

Baths³

et al. 2015

IJMLC

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“…The problem of factors that are considered as less important for disease diagnosis but still, according to medical literature, deserve to be included in diagnostics process, was mentioned in [6]. However the main goal of that paper was to compare classifiers for the detection of heart disease.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection for Classification Incorporating Less Meaningful Attributes in Medical Diagnostics

Wosiak

Zakrzewska

2014

Annals of Computer Science and Information Systems

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Abstract-In medical diagnostics there is a constant need of searching for new methods of attribute acquiring, but it is difficult to asses if these new features can support the existing ones and can be useful in medical inference. In the paper the methodology of discovering features which are less informative while considering independently, however meaningful for diagnosis making, is investigated. The proposed methodology can contribute to better use of attributes, which have not been considered in the diagnostics process so far. The experimental study, which concerns arterial hypertension as one of the civilization diseases demanding early detection and improved treatment is presented. The experiments confirmed that additional attributes enable obtaining the diagnostic results comparable to the ones received by using the most obvious features.

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“…The second class that have an rich amount of occurrences. Identifying the minority classes is important in various fields such as medicinal analysis [9], software faults detection [10], Finances [11], drug discovery [11] or bio-informatics [12]. The classical learning techniques are not adapted to imbalanced data sets.…”

Section: Imbalanced Big Datamentioning

confidence: 99%

Parallel processing on Big Data in the context of Machine Learning and Hadoop Ecosystem: A Survey

Brahmane¹,

R²

2018

IJET

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Emergent Big Data applications have become gradually more essential. In reality, a lot of institutes, businesses and in general entire society from diverse segments depend more and more on information take out from enormous quantity of raw information, statistics and numbers. On the other hand, in Big Data perspective, customary information methods and policies are not as much of capable. They prove a time-consuming receptiveness and are short of quantifiability, measurability, presentation and accurateness. To solve the composite Big Data constraints and difficulties, a large amount effort has been carried out. As an effect, different categories of packages, distributions and technologies have been developed. In this paper an evaluation is done, this studies recent technologies developed for Big Data. It aims to assist to choose and adopt the exact combination of diverse Big Data technologies according to their technological, scientific needs and particular applications requirements. It provides not only a worldwide sight of most important Big Data technologies but also relationship according to special organizational, classifications levels

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Computational intelligence for heart disease diagnosis: A medical knowledge driven approach

Cited by 221 publications

References 27 publications

Comparative Study of Classification Techniques (SVM, Logistic Regression and Neural Networks) to Predict the Prevalence of Heart Disease

Comparative Study of Classification Techniques (SVM, Logistic Regression and Neural Networks) to Predict the Prevalence of Heart Disease

Feature Selection for Classification Incorporating Less Meaningful Attributes in Medical Diagnostics

Parallel processing on Big Data in the context of Machine Learning and Hadoop Ecosystem: A Survey

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