Fuzzy Logic and Hybrid based Approaches for the Risk of Heart Disease Detection: State-of-the-Art Review

Kaur, Jagmohan; Khehra, Baljit Singh

doi:10.1007/s40031-021-00644-z

Cited by 20 publications

(8 citation statements)

References 46 publications

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“…The basis of the fuzzy logic system is the creation of a model that can think and make decisions by using data in input clusters [41]. All rules are evaluated in parallel, and the order of the rules is not important [42]. Our system was developed with 16 input attributes from the dataset of BRCA associated breast cancer patients and 1 output attribute with 5 features: pathogenic, likely-pathogenic, VUS, likely-benign and benign.…”

Section: Matlab and Mamdani's Fuzzy Inference Methodsmentioning

confidence: 99%

BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

Senturk

Tuncel

Dogan

et al. 2021

Genes

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Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients’ data were used to train the system using Mamdani’s Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network’s overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations’ risk assessment in breast cancers as well as a unique tool for personalized medicine software.

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Section: Matlab and Mamdani's Fuzzy Inference Methodsmentioning

confidence: 99%

BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

Senturk

Tuncel

Dogan

et al. 2021

Genes

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“…Kaur and Khehra [33] presented the study that intends to review several studies on FL and hybrid-based methods for assessing patient risk for heart disease. e current study lists articles from 2010 together with the power, operating system, accuracy rate, and other requirements employed in the diagnosis of heart disease using FL and hybrid-based techniques.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Neural Fuzzy Hybrid Rule-Based Inference System with Test Cases for Prediction of Heart Attack Probability

Jha

Henge

Mandal

et al. 2022

Mathematical Problems in Engineering

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Heart disease has reached to the number one position in last decade in terms of mortality rate, and more wretchedly, heart attack has affected life in 80% of the cases. Cardiac arrest is an incurable incongruity that requires special treatment and cure. It has been a key research area for many years, and the number of researchers across the globe is devoted toward finding the optimal solution to avoid the ill-effect of this disease. Along with predicting heart disease, if focus moves towards prevention of heart attack as well, then this could result in major life saver area for masses. This research work is fully devoted toward finding out the probability of heart attack so that people can take preventive measure before it hit the wall. This research proposed the neural fuzzy inference system (NFIS) to represent the training data formed from the n-dimensions of functions. The NFIS consists of error computing module to improve the learning instructions when the errors have been measured, initially the membership functions are defined, and the parameters of membership functions are activated and learnt through when needed for an operation. The proposed methodology has experimented with sample test cases on Cleveland heart disease dataset from University of California Irvine (UCI) repository with the integration of supporting dependable and nondependable parameters, causing-factors, and data-matrices. This research has integration more than 13000 fuzzification rules to generate best decision-making, normalization process, planting techniques to create the feasibility to compute the heart attack probability and achieved 94 percentage of accuracy. This research can be extendable to build auto-altering and advise system with integration hardware peripheral circuit devices.

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“…It was proved that the frequencies of risks are minimal after early diagnosis (13) . FL and Hybrid approaches for HD detection study were proposed to compare the performance of ML techniques (14) . A hybrid ML and DL system was proposed to detect heart disease by making use of KNN, where positive instances are measured at the initial stage and evaluation is done after pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

“…ANN-FAHP model was trained to classify the disease based on the feature sets matching. Biotic and Abiotic search was performed during the prediction of HD and the accuracy results attained up to 86% (14)(15)(16)(17) . TheADWFS method was proposed by the researchers by employing ADM and FST, where the imbalance dataset problem is solved, and the coronary syndrome is identified.…”

Section: Introductionmentioning

confidence: 99%

Early Detection and Classification of Heart Diseases by Employing IFCMML and 2L-C Model with I-GA Machine Learning Methods

Lakshmi¹,

Sujatha²

2023

IJST

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Objectives: To identify a new method for early detection and classification of Heart Diseases (HD); to improve the accuracy of the results by employing I-FCMML (Improved Fuzzy C-Means Machine Learning) and 2L-C (Two-Level Classifier) models along with the I-GA (Improved Genetic Algorithm) methods. Methods: The I-FCMML algorithm is utilized for feature selection and extraction. Machine learning techniques such as Ensembled Random Forest method (ERFM) and Robust Gradient Boost Method (RGBT) are employed to predict the likelihood of HD and 2L-C, I-GA is used to classify and detect the HD at a premature stage based on features like age, gender, blood pressure, etc. I-FCMML with 2L-C & I-GA extracts all the features from the dataset (Cleveland HD dataset from the Cleveland Clinic Foundation, Ohio, USA) which includes 303 observations, 14 features and selects the suitable function to perform disease classification and detection with high accuracy. To evaluate the performance of the proposed method, MATLAB is used for implementation. The results are compared with existing algorithms such as 3P-ANN, ANN-FAHP, ADWFS, EDSS, and FE-PCA. Findings: Early HD detection and classification is achieved with 96.02% accuracy, 95.80% sensitivity, 94.76% specificity, 95% precision, 94% recall, 0.90 True Positive, 0.87 True Negative, and 94.13% F-Score to detect and classify the HD in a robust manner, which is comparatively high than the existing methods. Novelty: According to the findings of the comprehensive study, the proposed new method I-FCMML with 2L-C & I-GA has the potential to provide accurate and competent detection and classification of HD at an early stage, which could help for timely treatment and management of HD patients, and it also outperforms the existing methods such as 3P-ANN, ANN-FAHP, ADWFS, EDSS, and FE-PCA.

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Fuzzy Logic and Hybrid based Approaches for the Risk of Heart Disease Detection: State-of-the-Art Review

Cited by 20 publications

References 46 publications

BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

Neural Fuzzy Hybrid Rule-Based Inference System with Test Cases for Prediction of Heart Attack Probability

Early Detection and Classification of Heart Diseases by Employing IFCMML and 2L-C Model with I-GA Machine Learning Methods

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