Rule extraction for fuzzy expert system to diagnose Coronary artery disease

Baihaqi, Wiga Maulana; Setiawan, Noor Akhmad; Ardiyanto, Igi

doi:10.1109/icitisee.2016.7803062

Cited by 17 publications

(11 citation statements)

References 16 publications

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“…The decision tree (C4.5) classifiers are non-parameteric supervised learning technique used for classification and regression.The aim of this technique is to create a model that predicts the value of a target variable by learning simple decision rules. Baihaqi et al [ 58 ] performed an experimental research to diagnose CAD using C4.5, and they successfully obtained accuracy of 78.95%. However, studies reveal that the C4.5 classifier is not a promising approach for continuous features [ 59 , 60 ].…”

Section: Methodology Proceduresmentioning

confidence: 99%

Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Mastoi

Wah

Raj

et al. 2018

Cardiology Research and Practice

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Coronary artery disease (CAD) is the most dangerous heart disease which may lead to sudden cardiac death. However, CAD diagnoses are quite expensive and time-consuming procedures which a patient need to go through. The aim of our paper is to present a unique review of state-of-the-art methods up to 2017 for automatic CAD classification. The protocol of review methods is identifying best methods and classifier for CAD identification. The study proposes two workflows based on two parameter sets for instances A and B. It is necessary to follow the proper procedure, for future evaluation process of automatic diagnosis of CAD. The initial two stages of the parameter set A workflow are preprocessing and feature extraction. Subsequently, stages (feature selection and classification) are same for both workflows. In literature, the SVM classifier represents a promising approach for CAD classification. Moreover, the limitation leads to extract proper features from noninvasive signals.

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Section: Methodology Proceduresmentioning

confidence: 99%

Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Mastoi

Wah

Raj

et al. 2018

Cardiology Research and Practice

View full text Add to dashboard Cite

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“…Baihaqi et al [80] applied the C4.5 classifier to diagnose CAD using and obtained 78.95% accuracy. However, the classifier C4.5 usually does not allow small datasets.…”

Section: Gain (Attribute X) = Entropy (Decision Attribute Y) − Entropmentioning

confidence: 99%

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

et al. 2021

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Cardiovascular diseases are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).

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“…This happened due to an error during data retrieval carried out by BMKG Cilacap, even though the retrieval process was carried out using radar or satellite, but there were still opportunities for errors. To handle empty data, we use the average for each attribute to fill in the blank data [27]. Likewise, on unmeasured data, the data is considered empty and is replaced by the average of the attribute.…”

Section: Datamentioning

confidence: 99%

Artificial Neural Network for Rainfall Prediction Base on Historical Rainfall Data by Day

2019

IJATCSE

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Increasingly erratic rainfall has a significant effect on agriculture, and health. One field of agriculture that utilizes rainfall patterns is paddy fields. Rice farmers need a calculation of rainfall to determine the process of hatching and planting rice, especially for farmers who have rainfed rice fields. While in the health sector, rainfall greatly determines the growth of mosquitoes which can lead to dengue fever. The health office needs calculations to anticipate the number of people affected by dengue fever. In this study two data modeling were made in predicting rainfall in the Banyumas area, one data model or scenario using weather data other than rainfall such as temperature, humidity, wind speed, duration of solar radiation. While scenario two only uses historical rainfall data per day. In making models and testing data using the Artificial Neural Network (ANN) algorithm. The results showed that scenario two was better than scenario one after being evaluated using the Root Mean Square Error (RMSE). The RMSE obtained in scenarios one and two are 21,667 and 20,448, respectively. It can be concluded that to predict rainfall in the Banyumas area it is better to use historical rainfall data per day compared to using other weather data.

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Rule extraction for fuzzy expert system to diagnose Coronary artery disease

Cited by 17 publications

References 16 publications

Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Artificial Neural Network for Rainfall Prediction Base on Historical Rainfall Data by Day

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