Feature Selection and Dwarf Mongoose Optimization Enabled Deep Learning for Heart Disease Detection

Balasubramaniam, S.; Kumar, K. Satheesh; Kavitha, V.; Prasanth, A.; Sivakumar, T. A.

doi:10.1155/2022/2819378

Cited by 23 publications

(4 citation statements)

References 23 publications

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“…Tools have been developed to build and enhance image analysis because of widespread issues with photos, such as speckle noise, clutter, and under-identification by the human eye. The healthcare sector is one of the most active in the modern economy, growing at rates of [4][5][6]. Several cutting-edge technologies, including the convolutional neural networks, Intelligence, and advanced analytics, have played vital role in this growth.…”

Section: Figure 1 Breast Cancer Structural Characteristics Compared T...mentioning

confidence: 99%

Impressive predictive model for Breast Cancer based on Machine Learning

Selvaraj,

Thangavel,

Prabhakaran

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Breast cancer is a major health concern for women all over the world. OBJECTIVES: In order to reduce mortality rates and provide the most effective treatment, Histopathology image prognosis is essential. When a pathologist examines a biopsy specimen under a microscope, they are engaging in histopathology. The pathologist looks for the picture, determines its type, labels it, and assigns a grade. METHODS: Tissue architecture, cell distribution, and cellular form all play a role in determining whether a histopathological scan is benign or malignant. Manual picture classification is the slowest and most error-prone method. Automated diagnosis based on machine learning is necessary for early and precise diagnosis, but this challenge has prevented it from being addressed thus far. In this study, we apply curvelet transform to a picture that has been segmented using k-means clustering to isolate individual cell nuclei. RESULTS: We analysed data from the Wisconsin Diagnosis Breast Cancer database for this article in the context of similar studies in the literature. CONCLUSION: It is demonstrated that compared to another machine learning algorithm, the IICA-ANN IICA-KNN and IICA-SVM-KNN method using the logistic algorithm achieves 98.04% accuracy.

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Section: Figure 1 Breast Cancer Structural Characteristics Compared T...mentioning

confidence: 99%

Impressive predictive model for Breast Cancer based on Machine Learning

Selvaraj,

Thangavel,

Prabhakaran

et al. 2024

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

show abstract

“…The majority of adult deaths can be attributed to this. By analyzing a person's medical records, our approach can identify those who are at high risk of being diagnosed with cardiovascular disease [3]. It helps doctors pinpoint patients having cardiac symptoms like chest pain or high blood pressure so they may administer a more precise diagnosis and more targeted treatment.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Machine Learning Classifiers for the Diagnosis of Heart Disease

Thangavel,

Selvaraj,

Karthikeyan V

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Preventable deaths from cardiovascular diseases outnumber all others combined. Detecting it at an early stage is crucial. Human lives will be saved as a result. OBJECTIVES: Improved cardiac disease prediction using machine learning classifiers is the focus of this article. METHODS: We have used many different classifiers, such as the support vector machine, naive bayes, random forest, and k-nearest neighbours, to achieve this goal, even though we can’t predict high accuracy in this classifier. So, we have proposed Hyper parameter adjustment was applied to the classifiers, which increased their precision. It was possible to compare the classifiers. RESULTS: In comparison to other machine learning classifiers, Logistic Regression achieves higher prediction accuracy, at 95.5%. CONCLUSION: To help people find the nearest cardiac care facilities, Google Maps has been integrated into a responsive web application that has been built for forecasting heart illness.

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“…Agushaka et al [38,39]. It has been used to address various real engineering optimizing issues because of its high globally searching ability and resilience [40][41][42][43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Dwarf Mongoose Optimization Algorithm for Solving Engineering Problems

et al. 2023

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This paper proposes a new Enhanced Dwarf Mongoose Optimization Algorithm (EDMOA) with an alpha-directed Learning Strategy (LS) for dealing with different mathematical benchmarking functions and engineering challenges. The DMOA’s core concept is inspired by the dwarf mongoose’s foraging behavior. The suggested algorithm employs three DM social categories: the alpha group, babysitters, and scouts. The family forages as a team, with the alpha female initiating foraging and determining the foraging course, distance traversed, and sleeping mounds. An enhanced LS is included in the novel proposed algorithm to improve the searching capabilities, and its updating process is partially guided by the updated alpha. In this paper, the proposed EDMOA and DMOA were tested on seven unimodal and six multimodal benchmarking tasks. Additionally, the proposed EDMOA was compared against the traditional DMOA for the CEC 2017 single-objective optimization benchmarks. Moreover, their application validity was conducted for an important engineering optimization problem regarding optimal dispatch of combined power and heat. For all applications, the proposed EDMOA and DMOA were compared to several recent and well-known algorithms. The simulation results show that the suggested DMOA outperforms not only the regular DMOA but also numerous other recent strategies in terms of effectiveness and efficacy.

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Feature Selection and Dwarf Mongoose Optimization Enabled Deep Learning for Heart Disease Detection

Cited by 23 publications

References 23 publications

Impressive predictive model for Breast Cancer based on Machine Learning

Impressive predictive model for Breast Cancer based on Machine Learning

Analyzing Machine Learning Classifiers for the Diagnosis of Heart Disease

An Enhanced Dwarf Mongoose Optimization Algorithm for Solving Engineering Problems

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