Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques

Nadakinamani, Rajkumar Gangappa; Reyana, A.; Kautish, Sandeep; Vibith, A. S.; Gupta, Yogita; Abdelwahab, Sayed F.; Mohamed, Ali Wagdy

doi:10.1155/2022/2973324

Cited by 63 publications

(31 citation statements)

References 32 publications

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“…Their studies cover many aspects of cardiac illness. In [ 33 ], the author applied the REP Tree, R Tree, M5P Tree, LR, J48, NB, and JRIP on Hungarian and Statlog datasets to classify CVD. RF, DT, and LR are applied in [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Hassan

Iqbal

Irfan

et al. 2022

Sensors

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Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.

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Section: Methodsmentioning

confidence: 99%

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Hassan

Iqbal

Irfan

et al. 2022

Sensors

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show abstract

“…Many of these algorithms is gaining widespread acceptance in different domains of machine learning and artificial inteligence, such as the use of such algorithms in neuro-evolution in Reinforcement Learning (RL) settings [87] or usage of evolutionary control parameters in Automated Machine Learning [87]. In particular, we have observed widespread use of AGSK in transportation problems [88], [89], path planning [90], knapsack problems [91], [92], electrochemical systems such as photo-voltaic cells [93], [94], engineering problems like fault diagnostics in power systems [95], as well as adoption in machine learning [96] and RL techniques [97]. As a result, it is reasonable to claim that constrained evolutionary algorithms offer enormous potential in the application of many engineering design challenges [1], [98]- [100].…”

Section: Applicationsmentioning

confidence: 99%

Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

Saha¹,

De²

2022

Preprint

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Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

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“…When the system is linked to the hospital's database, they may begin making adjustments. In [31], using machine learning techniques, CDPS aims to help professionals make better decisions and forecasts. Patients and physicians could benefit from additional investigation aimed at improving the CDPS model's performance of the classifier for a more cost-effective and time-saving solution.…”

Section: Iirelated Workmentioning

confidence: 99%

An Automated Heart Disease Diagnosis System Using Adaptive Cross-Layer Stacked Residual Convolutional Neural Networks

Kumar

Singh

2022

Preprint

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Heart disease is the devastating illness that kills the most people worldwide when compared to other ailments. Heart disease is a significant hazard. A cardiac problem occurs when the arteries supplying oxygen and blood to the heart become fully clogged or constricted. One of the primary causes of death has been heart disease. In a short period, the mortality rate has grown considerably. Because an incorrect diagnosis of the condition might result in death, accuracy and safe diagnosis of cardiac disease should be a top priority in healthcare. Cardiovascular disease may be avoided if the prognosis is right, but it can potentially be fatal if the prediction is incorrect. Hence, in this paper, we propose an automated heart disease diagnosis system. Initially, the patient data is collected and preprocessed. The features are extracted using Kernel-based Linear Discriminant Analysis (K-LDA). (Correlation-based feature selection) CFS is performed to extract relevant features. We propose Adaptive Cross-Layer Stacked Residual Convolutional Neural Networks (ACLS-RCNN) for the prediction of the disease. We also introduce the Improved Cuttlefish-Swarm Optimization Algorithm (ICSOA) for enhancing the prediction process. The performance metrics like accuracy, precision, recall, and f1-score of the proposed system are evaluated using the python simulation tool and compared with conventional methodologies. Our framework can make a very accurate prediction based on clinical data and diagnoses from the real world.

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Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques

Cited by 63 publications

References 32 publications

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

An Automated Heart Disease Diagnosis System Using Adaptive Cross-Layer Stacked Residual Convolutional Neural Networks

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