Expert system for heart disease based on electrocardiogram data using certainty factor with multiple rule

Sumiati, Sumiati; Saragih, Hoga; Rahman, Titik Abdul; Triayudi, Agung

doi:10.11591/ijai.v10.i1.pp43-50

Cited by 15 publications

(14 citation statements)

References 32 publications

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“…The performance metrics employed for evaluation of the developed model include accuracy and confusion. Accuracy and confusion matrix are among the most widely used performance measures used by researchers for evaluation of machine learning model on classification task [12], [14], [15]. Accuracy is defined as the ration of true predictions to total samples predicted by a given model [16], [17], [19].…”

Section: Performancementioning

confidence: 99%

A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

Suresh

Assegie²,

Rajkumar

et al. 2022

IJECE

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Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.

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Section: Performancementioning

confidence: 99%

A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

Suresh

Assegie²,

Rajkumar

et al. 2022

IJECE

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show abstract

“…The most frequently used performance metric based on the confusion matrix for classification is accuracy. Accuracy is the ratio of true predictions (TP and TN) with the overall data that describes the level of closeness of the predicted value to the actual value, as shown in (8). In the training phase, the model's accuracy is obtained from the average of each fold in the cross-validation.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…There is an adequate need for the early detection of patients with CAD. A machine learning approach can solve problems in the biomedical domain [5]- [8]. Machine learning gives the computer ability to learn and improve from experience automatically.…”

Section: Introductionmentioning

confidence: 99%

Effective predictive modelling for coronary artery diseases using support vector machine

Nugroho

Sukmadewa

Vidianto

et al. 2022

IJ-AI

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Coronary artery disease (CAD) is a category of cardiovascular disease that causes the highest mortality rate in the world. CAD occurs due to plaque build-up on the walls of the arteries that supply blood to the heart and other organs of the body. To control the mortality rate, a practical model that is capable of predicting CAD is needed. Machine learning approaches have been used in solving various problems in various domains, including biomedicine. However, real-world data often has an unbalanced class distribution that can interfere with classifier performance. In addition, data has many features to process. This study focuses on effective modeling capable of predicting CAD using feature selection to handle high dimensional data and feature resampling to handle unbalanced data. Feature selection is very effective by eliminating irrelevant features from the training data. Hyperparameter tuning is also done to find the best combination of parameters in support vector machines (SVM). Our results show that the SVM cross-validated ten times has a more accurate training result. Furthermore, the grid search on SVM cross-validated ten times had more accurate training model results and achieved 88% accuracy on the test data.

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“…The result of this research is an expert system that can generate the name of the periodontal disease and the degree of certainty of the user's disease. In addition, research [10] aims to develop system or software that can replace doctors for the identification process heart defects based on expert system. The expert system was developed with certainty factor with a double rule approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Expert System for Diagnosis of Uterine Myomas using the Certainty Factor Method

Putra

Ulum

Aryani

2021

Int. J. Eng. Scie. and Inform. Technology.

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An expert system which is part of artificial intelligence is a computer system that is able to imitate the reasoning of an expert with certain expertise. An expert system in the form of software can replace the role of an expert (human) in the decision-making process based on the symptoms given to a certain level of certainty. This study raises the problem that many women experience, namely not understanding that they have uterine myomas. Many women do not understand and are not aware that there are already symptoms that are felt and these symptoms are symptoms of the presence of uterine myomas in their bodies. Therefore, it is necessary for women to be able to diagnose independently so that they can take treatment as quickly as possible. In this study, the expert will first provide the expert CF values. Then the user / respondent gives an assessment of his condition with the CF User values. In the end, the values obtained from these two factors will be processed using the certainty factor formula. Users must provide answers to all questions given by the system in accordance with their current conditions. After all the conditions asked are answered, the system will display the results to identify that the user is suffering from uterine myoma disease or not. The Expert System with the certainty factor method was tested with a patient who entered the symptoms experienced and got the percentage of confidence in uterine myomas/fibroids of 98.70%. These results indicate that an expert system with the certainty factor method can be used to assist in diagnosing uterine myomas as early as possible.

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Expert system for heart disease based on electrocardiogram data using certainty factor with multiple rule

Cited by 15 publications

References 32 publications

A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

Effective predictive modelling for coronary artery diseases using support vector machine

Expert System for Diagnosis of Uterine Myomas using the Certainty Factor Method

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