Early Detection of Heart Syndrome Using Machine Learning Technique

Basha, N. Sardar; S, Ashok Kumar P; C, Gopal Krishna; Venkatesh, Prashanth

doi:10.1109/iceeccot46775.2019.9114651

Cited by 25 publications

(15 citation statements)

References 4 publications

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“…The result in [74] showed that the SVM algorithm is better than KNN On the other hand, in [76] KNN algorithm override SVM by about 3%. In research [76], the decision tree's accuracy is better than the random forest, around 1%. Nevertheless, in [86], the random forest's accuracy exceeds around 12% than the decision tree.…”

Section: Discussionmentioning

confidence: 97%

“…The logistic regression algorithm's accuracy and sensitivity results are being selected as the best algorithm among the others. In [74] and [76], KNN and SVM algorithms were applied to two different datasets and features. The result in [74] showed that the SVM algorithm is better than KNN On the other hand, in [76] KNN algorithm override SVM by about 3%.…”

Section: Discussionmentioning

confidence: 99%

“…In [74] and [76], KNN and SVM algorithms were applied to two different datasets and features. The result in [74] showed that the SVM algorithm is better than KNN On the other hand, in [76] KNN algorithm override SVM by about 3%. In research [76], the decision tree's accuracy is better than the random forest, around 1%.…”

Section: Discussionmentioning

confidence: 99%

“…In 2019, Noor Basha and et al [76] proposed a heart disease model to detect and analyze heart syndrome with several parameters such as blood pressure, diabetes, heart rate, age, and gender. Data scientists will do predictive work on massive data for early analysis of heart syndrome to save patients' lives.…”

Section: H Deep Learningmentioning

confidence: 99%

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The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

155

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Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naïve Bayes, logistic regression, support vector machine, K-nearest neighbor, K-means clustering, decision tree, and random forest. In this work, many previous studies were reviewed that used machine learning algorithms for detecting various diseases in the medical area in the last three years. A comparison is provided concerning these algorithms, assessment processes, and the obtained results. Finally, a discussion of the previous works is presented.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: H Deep Learningmentioning

confidence: 99%

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The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

155

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“…As demonstrated in Table 5, the proposed model outperforms compared to the existing work. [5] 2019 NB, RF 86.81% Wan Hajarul [6] 2018 DT and RF 82.99% with RF Amin Ul Haq [8] 2018 SVM, DT, RF, NB, DT 86% with SVM Kathleen H. Miaoa [11] 2018 Deep neural network 83.67% Wiharto Wiharto [12] 2019 Ensemble classifier 88.33% Noor Basha [18] 2019 KNN, NB, SVM, DT 85%, with KNN Edsel Ing [19] 2019 SVM and LR 82.71% with LR Márcio Dias [20] 2020 SVM 87.71% Khaled Mohamad [21] 2020 SVM, NB 84.19% with SVM Pooja Rani [22] 2021 NB, LR, NB, SVM, RF 84.79% with SVM Suja Panicker [23] 2020 SVM 90% G. Magesh [24] 2020 RF 89.30% Ashir Javeed [25] 2020 Deep neural network 91.83% G. Saranya [ A hybrid approach to medical decision-making: diagnosis of heart disease … (Tamilarasi Suresh) 1837 5. CONCLUSION Automated intelligent approaches are crucial for timely prediction of heart disease.…”

Section: Comparative Studymentioning

confidence: 99%

A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

Suresh

Assegie²,

Rajkumar

et al. 2022

IJECE

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Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.

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Review of Machine Learning Techniques for Analysis of Medical Data Sets

Bhende,

Sakarkar

2022

Rising Threats in Expert Applications and Solutions

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Early Detection of Heart Syndrome Using Machine Learning Technique

Cited by 25 publications

References 4 publications

The Role of Machine Learning Algorithms for Diagnosing Diseases

The Role of Machine Learning Algorithms for Diagnosing Diseases

A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

Review of Machine Learning Techniques for Analysis of Medical Data Sets

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