An accurate fuzzy rule-based classification systems for heart disease diagnosis

Bahani, Khalid; Moujabbir, Mohammed; Ramdani, Mohammed

doi:10.1016/j.sciaf.2021.e01019

Cited by 17 publications

(9 citation statements)

References 28 publications

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“…Based on Table II, several of the research works applied publicly available datasets [10,11,22,23,24,25,27,28], whereas the remaining research works used collected dataset from implemented research [13,21,9,12,20,34]. This indicates the significance of developing and validating datasets by medical experts to provide acceptance and usable models to improve decision-making.…”

Section: Medical Models For Decision-makingmentioning

confidence: 99%

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Classification and Prediction Evaluation of a Fuzzy Rule-Based Model for Diabetes Diagnosis, Level of Care and Pima Indians Diabetes Datasets

Teh Noranis Mohd Aris

2023

IJRITCC

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— Classification and prediction of diseases is crucial in decision-making for the healthcare sectors, especially diabetes, a chronic disease. The availability and accessibility of diabetes datasets assists medical practitioners in the diagnosis process as well as researchers in various fields and these datasets are valuable sources. However, diabetes datasets are exposed to vagueness and uncertainty issues. This research work improved an interrelated decision-making model for diabetes by proposing a fuzzy rule-based model to handle the vagueness and uncertainty issues. The research methodology starts with pre-processing of the simulated diabetes diagnosis and level of care datasets that were validated by medical experts, as well as the Pima Indians Diabetes Dataset (PIDD). This is followed by the design of the fuzzy model and the construction of the fuzzy rules. Next, the testing of the fuzzy model using six supervised machine learning algorithms namely J48, Logistic, Naive Bayes Updateable, Random Tree, Bayes Net and AdaBoostM1. Lastly, the evaluation of the fuzzy model in terms of the accuracy, precision, recall, F1-Score and confusion matrix. Experimental results show 100% accuracy for the diabetes diagnosis fuzzy model, for all the five machine learning algorithms mentioned except AdaBoostM1 with 79.8165% accuracy. In addition, for the level of care fuzzy model, the highest accuracy produced is 97.1098% for J48 algorithm and the lowest accuracy is 93.1049% for Naive Bayes Updateable and Bayes Net algorithms. Furthermore, for the PIDD fuzzy model, the highest accuracy obtained is 74.8698% for J48 and AdaBoostM1 algorithms and the lowest accuracy is 70.1823% for Random Tree algorithm. Overall, the proposed fuzzy model produced a good accuracy and working as expected associated to the previous interrelated decision-making model.

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Section: Medical Models For Decision-makingmentioning

confidence: 99%

“…Moreover, machine learning is not restricted to any comprehensive framework which allows researchers to extend and improve the models [9]. Research in ML and data mining has contributed to medical diagnosis [10,11,12]. This research work is motivated due to the importance of classification and prediction for diabetes diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Classification and Prediction Evaluation of a Fuzzy Rule-Based Model for Diabetes Diagnosis, Level of Care and Pima Indians Diabetes Datasets

Teh Noranis Mohd Aris

2023

IJRITCC

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“…On the other hand, machine learning methods are based on the training of the model with the help of incremental learning and dataset variation. Fuzzy rule‐based models are successful in many classification problems‐ Sentiment Analysis [5], Speech Emotion Recognition [6, 7], Big Data classification [8], and heart disease [9, 10]. There are many machine learning models [11] available to detect cancer of several types like Naive Bayes [12, 13], Support Vector Machine (SVM) [14, 15], Least square‐ SVM [16], K‐nearest neighbours [17, 18], Decision Tree [19, 20], Convolution Neural networks [21, 22] etc.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy rule‐based system with decision tree for breast cancer detection

Gupta

Gaur²,

Vashishtha³

et al. 2023

IET Image Processing

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Breast cancer is possibly the deadliest illness in the world and the risks are gradually increasing. One out of eight women has the chance to be detected with breast cancer in their lifetime. The utmost cause for the higher fatality rates is the prolonged prognosis for the detection of breast cancer. The focus of this study is therefore to develop a better fuzzy expert system for the detection of breast cancer using decision tree analysis for deriving the rule base. For this classification problem, the input features of the dataset are converted into human‐understandable terms‐linguistic variables. The Mamdani Fuzzy Rule‐Based system is deployed as the main inference engine and the centroid method for the defuzzification process to convert the final fuzzy score into class labels‐ benign (not cancerous) or malignant (cancerous). A decision tree algorithm is applied the creating a novel set of 27 fuzzy rules which are fed into FRBS. The investigation is performed on the publicly available Wisconsin Breast Cancer Dataset. The accuracy obtained by the proposed system is about 97%, recall is 99.58% and precision is about 93%. The experiments on this dataset yield higher performance as compared to the state‐of‐the‐art dataset.

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“…For example, Polat et al rst obtained new values for each attribute based on the k-nearest neighbor method and the authors performed an arti cial immune recognition system with fuzzy resource allocation mechanism-based classi cation to detect heart disease [12]. In another study, Bahani et al proposed a method based on a fuzzy rule-based classi cation [13]. Gárate-Escamila et al performed experiments with 6 classi ers on the features which are the combination of chi-square and principal component analysis on the heart disease dataset downloaded from the UCI Machine Learning Repository This study was conducted with the motivation of applying machine learning techniques to CAD data in order to develop decision support systems that give support to specialist doctors in their elds.…”

Section: Introductionmentioning

confidence: 99%

Coronary artery disease classification using support vector machines tuned via randomized search cross-validation

Akyol

2023

Preprint

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Coronary artery disease outstands health problem that causes high mortality in the world population. This disease brings with it fateful problems such as heart attack and heart failure in patients with cardiovascular problems. Early diagnosis of coronary artery disease is essential for the timely administration of the right treatment and reduction of mortality. Angiography is the most preferred method for CAD detection. However, the complications and costs of this method have led researchers to forage alternative methods through machine learning algorithms. By developing a machine learning model with high generalization ability, prediction errors can be minimized. Thus, these models could potentially be useful for specialist physicians in the effective detection of coronary artery disease. The main focus of this study is to perform coronary artery disease detection with improved support vector machines. k-fold cross-validation experiments were performed on the Z-Alizadeh Sani dataset to evaluate the performance of the models. According to the results obtained, support vector machines with randomized search cross-validation provided the best performance when compared to other models. 87.102% average accuracy, 91.176% average sensitivity, 90.852% average precision, 76.996% average specificity, and also 8.824% average false negative rate obtained by 5-fold cross-validation competes with the known approaches in the literature.

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An accurate fuzzy rule-based classification systems for heart disease diagnosis

Cited by 17 publications

References 28 publications

Classification and Prediction Evaluation of a Fuzzy Rule-Based Model for Diabetes Diagnosis, Level of Care and Pima Indians Diabetes Datasets

Classification and Prediction Evaluation of a Fuzzy Rule-Based Model for Diabetes Diagnosis, Level of Care and Pima Indians Diabetes Datasets

A fuzzy rule‐based system with decision tree for breast cancer detection

Coronary artery disease classification using support vector machines tuned via randomized search cross-validation

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