Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis

Ghwanmeh, Sameh; Mohammad, Adel Hamdan; Al-Ibrahim, Ali

doi:10.4236/jilsa.2013.53019

Cited by 73 publications

(30 citation statements)

References 12 publications

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“…e investigation techniques in early stages used to identify heart disease were complicated, and its resulting complexity is one of the major reasons that affect the standard of life [4]. e heart disease diagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of diagnostic apparatus and shortage of physicians and others resources which affect proper prediction and treatment of heart patients [5]. e accurate and proper diagnosis of the heart disease risk in patients is necessary for reducing their associated risks of severe heart issues and improving security of heart [6].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

Haq

Memon

et al. 2018

Mobile Information Systems

425

182

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Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart disease is important for heart failure prevention and treatment. The diagnosis of heart disease through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart disease, noninvasive-based methods such as machine learning are reliable and efficient. In the proposed study, we developed a machine-learning-based diagnosis system for heart disease prediction by using heart disease dataset. We used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and seven classifiers performance evaluation metrics such as classification accuracy, specificity, sensitivity, Matthews’ correlation coefficient, and execution time. The proposed system can easily identify and classify people with heart disease from healthy people. Additionally, receiver optimistic curves and area under the curves for each classifier was computed. We have discussed all of the classifiers, feature selection algorithms, preprocessing methods, validation method, and classifiers performance evaluation metrics used in this paper. The performance of the proposed system has been validated on full features and on a reduced set of features. The features reduction has an impact on classifiers performance in terms of accuracy and execution time of classifiers. The proposed machine-learning-based decision support system will assist the doctors to diagnosis heart patients efficiently.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

Haq

Memon

et al. 2018

Mobile Information Systems

425

182

View full text Add to dashboard Cite

show abstract

“…Few research works are for recognizing ultra-sonogram patterns ( [2], [3], [4], [5], [6], [7]). In [2],a novel fuzzy feature extraction method to encode local texture which extends the local binary pattern approach by incorporating fuzzy logic in the representation of local patterns of texture in ultrasound images is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Optimize interdisciplinary characters between medicine and computing and also optimize ultrasound modality that is best for brain scanning. Paper [7] proposed a method based on artificial neural networks that provides a decision support system to identify three main heart diseases: mitral stenosis, aortic stenosis and ventricular septal defect and the system deals with an encouraging opportunity to develop an operational screening and testing device for heart disease diagnosis and can deliver great assistance for clinicians to make advanced heart diagnosis. The authors have evaluated Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Detection of Hepatomegaly by Automated-Diagnosis Method based on Artificial Neural Network

Roy¹,

Turna²,

Hossain³

et al. 2020

IJCA

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Diagnosis sonography (ultrasonography) is an ultrasoundbased diagnostic imaging technique which is used for visualizing internal body structures including tendons, muscles, joints, vessels and internal organs for possible pathology. In Bangladesh, there is no formal training for the sonographer, that's why sometimes there is a great possibility to make mistakes to find out the exact disease. Sonography is done manually. Sometimes the disease suggested by doctor and the diagnosis report that is done by the sonographer are not the same, for this reason the patient may be confused about his or her actual disease. To solve all these problems an automatic diagnosis tool is needed to get the required result of the required patient. Here, proposed an artificial neural network based model for automated diagnosis of hepatomegaly from ultrasound images to get an automated result which is appropriate for anyone.

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“…Researchers try to come across an efficient technique for the detection of heart disease, as the current diagnosis techniques of heart disease are not much effective in early time identification due to several reasons, such as accuracy and execution time [3]. The diagnosis and treatment of heart disease is extremely difficult when modern technology and medical experts are not available [4]. The effective diagnosis and proper treatment can save the lives of many people [5].…”

Section: Introductionmentioning

confidence: 99%

Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare

et al. 2020

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Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.

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Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis

Cited by 73 publications

References 12 publications

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

Detection of Hepatomegaly by Automated-Diagnosis Method based on Artificial Neural Network

Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare

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