An Integrated Algorithmic MADM Approach for Heart Diseases’ Diagnosis Based on Neutrosophic Hypersoft Set with Possibility Degree-Based Setting

Rahman, Atiqe Ur; Saeed, Muhammad; Mohammed, Mazin Abed; Krishnamoorthy, Sujatha; Kadry, Seifedine; Eid, Fatma

doi:10.3390/life12050729

Cited by 18 publications

(14 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e relationship between the predicted heart disease value (H d ) and patient age (A c ) is shown in Figure 4, and equation (3) shows the relationship between H d and A c :…”

Section: Curve Fitting Techniquementioning

confidence: 99%

See 1 more Smart Citation

Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models

Kumar

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressure, high cholesterol, and smoking significantly increase the risk of heart disease. To estimate the risk of heart disease is a complex process because it depends on various input parameters. The linear and analytical models failed due to their assumptions and limited dataset. The existing studies have used medical data for classification purposes, which help to identify the exact condition of the patient, but no one has developed any correlation equation which can be directly used to identify the patients. In this paper, mathematical models have been developed using the medical database of patients suffering from heart disease. Curve fitting and artificial neural network (ANN) have been applied to model the condition of patients to find out whether the patient is suffering from heart disease or not. The developed curve fitting model can identify the cardiac patient with accuracy, having a coefficient of determination (R2-value) of 0.6337 and mean absolute error (MAE) of 0.293 at a root mean square error (RMSE) of 0.3688, and the ANN-based model can identify the cardiac patient with accuracy having a coefficient of determination (R2-value) of 0.8491 and MAE of 0.20 at RMSE of 0.267, it has been found that ANN provides superior mathematical modeling than curve fitting method in identifying the heart disease patients. Medical professionals can utilize this model to identify heart patients without any angiography or computed tomography angiography test.

show abstract

“…e relationship between the predicted heart disease value (H d ) and patient age (A c ) is shown in Figure 4, and equation (3) shows the relationship between H d and A c :…”

Section: Curve Fitting Techniquementioning

confidence: 99%

“…Chronic disease prediction is essential in healthcare informatics [ 2 ]. It is important to detect the sickness as soon as possible [ 3 ]. Heart disease and diabetes risk may be estimated using machine learning algorithms that analyze the data for these diseases.…”

Section: Introductionmentioning

confidence: 99%

Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models

Kumar

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…The wavelet transforms technique is one of the most commonly used to remove noise and artifacts from ECG signals [ 5 , 6 , 7 ]. There have been several algorithms previously proposed to extract waveforms from the ECG cycle [ 8 , 9 , 10 , 11 ]. The authors in [ 12 ] proposed a rapid ramp effective algorithm to determine the occurrence of the R-peaks from ECG signals.…”

Section: Introductionmentioning

confidence: 99%

Novel DERMA Fusion Technique for ECG Heartbeat Classification

Mastoi

Wah

Mohammed

et al. 2022

Life

Self Cite

View full text Add to dashboard Cite

An electrocardiogram (ECG) consists of five types of different waveforms or characteristics (P, QRS, and T) that represent electrical activity within the heart. Identification of time intervals and morphological appearance of the waves are the major measuring instruments to detect cardiac abnormality from ECG signals. The focus of this study is to classify five different types of heartbeats, including premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), PACE, and atrial premature contraction (APC), to identify the exact condition of the heart. Prior to the classification, extensive experiments on feature extraction were performed to identify the specific events from ECG signals, such as P, QRS complex, and T waves. This study proposed the fusion technique, dual event-related moving average (DERMA) with the fractional Fourier-transform algorithm (FrlFT) to identify the abnormal and normal morphological events of the ECG signals. The purpose of the DERMA fusion technique is to analyze certain areas of interest in ECG peaks to identify the desired location, whereas FrlFT analyzes the ECG waveform using a time-frequency plane. Furthermore, detected highest and lowest components of the ECG signal such as peaks, the time interval between the peaks, and other necessary parameters were utilized to develop an automatic model. In the last stage of the experiment, two supervised learning models, namely support vector machine and K-nearest neighbor, were trained to classify the cardiac condition from ECG signals. Moreover, two types of datasets were used in this experiment, specifically MIT-BIH Arrhythmia with 48 subjects and the newly disclosed Shaoxing and Ningbo People’s Hospital (SPNH) database, which contains over 10,000 patients. The performance of the experimental setup produced overwhelming results, which show around 99.99% accuracy, 99.96% sensitivity, and 99.9% specificity.

show abstract

“…Saqlain et al [ 30 , 31 , 32 ] discussed decision-making techniques for neutrosophic

-set with the help of aggregation operators and accuracy functions. Recently, Rahman et al [ 33 , 34 ] made significant additions in the literature of

-set by using its hybridized models in medical diagnosis and material selection, respectively.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy Parameterized Fuzzy Hypersoft Set and Riesz Summability Approach Based Decision Support System for Diagnosis of Heart Diseases

et al. 2022

Self Cite

View full text Add to dashboard Cite

Fuzzy parameterized fuzzy hypersoft set (Δ-set) is more flexible and reliable model as it is capable of tackling features such as the assortment of attributes into their relevant subattributes and the determination of vague nature of parameters and their subparametric-valued tuples by employing the concept of fuzzy parameterization and multiargument approximations, respectively. The existing literature on medical diagnosis paid no attention to such features. Riesz Summability (a classical concept of mathematical analysis) is meant to cope with the sequential nature of data. This study aims to integrate these features collectively by using the concepts of fuzzy parameterized fuzzy hypersoft set (Δ-set) and Riesz Summability. After investigating some properties and aggregations of Δ-set, two novel decision-support algorithms are proposed for medical diagnostic decision-making by using the aggregations of Δ-set and Riesz mean technique. These algorithms are then validated using a case study based on real attributes and subattributes of the Cleveland dataset for heart-ailments-based diagnosis. The real values of attributes and subattributes are transformed into fuzzy values by using appropriate transformation criteria. It is proved that both algorithms yield the same and reliable results while considering hypersoft settings. In order to judge flexibility and reliability, the preferential aspects of the proposed study are assessed by its structural comparison with some related pre-developed structures. The proposed approach ensures that reliable results can be obtained by taking a smaller number of evaluating traits and their related subvalues-based tuples for the diagnosis of heart-related ailments.

show abstract

An Integrated Algorithmic MADM Approach for Heart Diseases’ Diagnosis Based on Neutrosophic Hypersoft Set with Possibility Degree-Based Setting

Cited by 18 publications

References 23 publications

Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models

Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models

Novel DERMA Fusion Technique for ECG Heartbeat Classification

A Novel Fuzzy Parameterized Fuzzy Hypersoft Set and Riesz Summability Approach Based Decision Support System for Diagnosis of Heart Diseases

Contact Info

Product

Resources

About