A Systematic Review on Prediction Techniques for Cardiac Disease

Wadhawan, Savita; Maini, Raman

doi:10.4018/ijitsa.290001

Cited by 7 publications

(4 citation statements)

References 91 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of them are presented in [20,21]. A group of researchers in [22][23][24][25][26][27][28][29][30][31] works in the prediction of heart diseases. Another group of researchers in [32][33][34][35][36][37][38][39] focuses on the diagnosis of heart diseases.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of heart diseases: A fuzzy-logic-based approach

Ali,

Sadi,

Goni

2024

PLoS ONE

View full text Add to dashboard Cite

Cardiovascular diseases (CVD) also known as heart disease are now the leading cause of death in the world. This paper presents research for the design and creation of a fuzzy logic-based expert system for the prognosis and diagnosis of heart disease that is precise, economical, and effective. This system entails a fuzzification module, knowledge base, inference engine, and defuzzification module where seven attributes such as chest pain type, HbA1c (Haemoglobin A1c), HDL (high-density lipoprotein), LDL (low-density lipoprotein), heart rate, age, and blood pressure are considered as input to the system. With the aid of the available literature and extensive consultation with medical experts in this field, an enriched knowledge database has been created with a sufficient number of IF-THEN rules for the diagnosis of heart disease. The inference engine then activates the appropriate IF-THEN rule from the knowledge base and determines the output value using the appropriate defuzzification technique after the fuzzification module fuzzifies each input depending on the appropriate membership function. Moreover, the fusion of web-based technology makes it suitable and cost-effective for the prognosis of heart disease for a patient and then he can take his decision for addressing the problem based on the status of his heart. On the other hand, it can also assist a medical practitioner to reach a more accurate conclusion regarding the treatment of heart disease for a patient. The Mamdani inference method has been used to evaluate the results. The system is tested with the Cleveland dataset and cross-checked with the in-field dataset. Compared with the other existing expert systems, the proposed method performs 98.08% accurately and can make accurate decisions for diagnosing heart diseases.

show abstract

Section: Related Workmentioning

confidence: 99%

Diagnosis of heart diseases: A fuzzy-logic-based approach

Ali,

Sadi,

Goni

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…These statistics highlight the urgent need for effective prevention and treatment strategies to combat this public health crisis. [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Automatic Data-Driven Classification Systems for Cardiovascular Disease

Jayaraman,

Pichai

2024

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

Cardiovascular disease (CVD) continues to contribute significantly to preventable deaths and avoidable disability worldwide. Prediction and prevention are of utmost importance in the support of public health. Machine learning and deep learning algorithms have emerged as powerful tools to improve the accuracy of diagnosis, prognosis, and treatment of cardiovascular disease. By employing these technologies, medical professionals can gain valuable insights into the risk factors associated with CVD. The focus of this research is to classify and predict cardiovascular diseases using techniques such as support vector machines, ensemble methods, decision trees, random forests, and neural networks. The effectiveness of these algorithms is evaluated based on metrics including accuracy, sensitivity, specificity, area under the curve (AUC), and F1 score. Results show that support vector machines and ensemble methods offer superior accuracy, while neural networks exhibit higher sensitivity and specificity in predicting cardiovascular diseases.

show abstract

“…Since patients may not always have ready access to such expertise and the precision required for the diagnosis may render it prone to human error, there has been a lot of research interest in the development of expert systems based on machine intelligence that can automate this process and decrease the risk of human error during the examination and diagnosis stage. Such intelligent systems can be built with the use of artificial intelligence (AI), specifically deep learning (Hong et al, 2020; H. Li et al, 2021), machine learning (Wadhawan & Maini, 2022) and have proven to be effective tools for the prognosis and diagnosis of different cardiac diseases (Iqtidar et al, 2021), analysis and interpretation of medical images (Lara Hernandez et al, 2021), etc. Machine learning techniques such as support vector machine (SVM), k‐nearest neighbour (kNN), decision tree (DT), and logistic regression (LR), are widely used among others (Juhola et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

EBPSO: Enhanced binary particle swarm optimization for cardiac disease classification with feature selection

Wadhawan

Maini

2022

Expert Systems

Self Cite

View full text Add to dashboard Cite

Cardiac disease is one of the leading causes of death worldwide, and its early detection and diagnosis can considerably increase the lifespan of patients. An automated expert system for early and accurate diagnosis that offsets human error can be designed using machine intelligence and appropriate pre-processing of data to ensure accuracy. To that end, enhanced binary particle swarm optimization (EBPSO) has been investigated in this paper to enable the definitive classification of cardiac disease with the aid of feature selection. The primary objective of this study is to improve the accuracy of the classification and the convergence speed while employing a lesser number of features. In order to achieve this, a new adaptive inertia weight was introduced to balance the exploitation and exploration capability of binary particle swarm optimization (BPSO). In addition, a novel idea called 'neighbourhood best' is introduced in the velocity update equation to improve the convergence speed. The proposed EBPSO approach was tested over well-known datasets of heart disease, that is, the Cleveland dataset, Hungarian dataset, Switzerland dataset, and Long-Beach-Va datasets. For classification purposes, two well-known classifiers, k-nearest neighbour, and support vector machine were used with the EBPSO. Furthermore, the performance of EBPSO was compared with the traditional BPSO, as well as with BPSO with proposed inertia weight (W a -BPSO) and other PSO variants. It has been observed that the proposed approach EBPSO with KNN shows remarkable efficiency in terms of average classification accuracy and convergence rate. The average classification accuracy for Cleveland dataset is 92.814%, for the Hungarian dataset is 91.97%, for the Switzerland dataset is 91.626% with EBPSO+KNN and for the Long-Beach-Va dataset is 85.253% with EBPSO + SVM. Additionally, the performance of W a -BPSO was compared with other commonly used inertia weights. The results of this study demonstrated that the proposed approach produces better results in terms of both the accuracy of classification as well as the convergence rate with a reduced feature set.

show abstract

A Systematic Review on Prediction Techniques for Cardiac Disease

Cited by 7 publications

References 91 publications

Diagnosis of heart diseases: A fuzzy-logic-based approach

Diagnosis of heart diseases: A fuzzy-logic-based approach

Automatic Data-Driven Classification Systems for Cardiovascular Disease

EBPSO: Enhanced binary particle swarm optimization for cardiac disease classification with feature selection

Contact Info

Product

Resources

About