HRV based feature selection for congestive heart failure and normal sinus rhythm for meticulous presaging of heart disease using machine learning

Aggarwal, Ritu; Kumar, Suneet

doi:10.1016/j.measen.2022.100573

Cited by 7 publications

(1 citation statement)

References 12 publications

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“…Calculate the weight -i(Dj). It is used for solving the problem according to different samples [33].…”

Section: Methodsmentioning

confidence: 99%

Classification Model for Meticulous Presaging of Heart Disease Detection through SDA and NCA using Machine learning :CMSDANCA

Aggarwal¹,

Kumar²

2022

IJRITCC

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For the design and implementation of CDSS, computation time and prognostic accuracy are very important. To analyze the large collection of a dataset for detecting and diagnosis disease ML techniques are used. According to the reports of World Health Organizations, HD is a major cause of death and killer in urban and rural areas or worldwide. The main reason for this is a shortage of doctors and delay in the diagnosis. In this research work, heart disease is a diagnosis by the data mining techniques and used the clinical parameters of patients for early stages diagnosis. The intend of this learning to develop a representation that relies on the prediction method for coronary heart disease. This proposed work used the approach of self-diagnosis Algorithm, Fuzzy Artificial neural network, and NCA & PCA and imputation methods. By the use of this technique computation time for prediction of Coronary HD can be reduced. For the implementation of this the two datasets are using such as Cleveland and Statlog datasets that is collected from the UCI kaggle the ML repository. The datasets for the disease prediction measure are used to accurately calculate the difference between variables and to determine whether they are correlated or not. For this classification model, the performance measure is calculated in requisites of their accuracy, precision, recall, and specificity. This approach is evaluated on the heart disease datasets for improving the accuracy performance results obtained. The outcome for KNN+SDA+NCA+FuzzyANN for Cleveland dataset accuracy achieved 98.56 %.and for Statlog dataset 98.66 %..

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“…Calculate the weight -i(Dj). It is used for solving the problem according to different samples [33].…”

Section: Methodsmentioning

confidence: 99%

Classification Model for Meticulous Presaging of Heart Disease Detection through SDA and NCA using Machine learning :CMSDANCA

Aggarwal¹,

Kumar²

2022

IJRITCC

View full text Add to dashboard Cite

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Pre-eclampsia Risk Factors Association with Cardiovascular Disease Prediction and Diagnosing Using Machine Learning

Aggarwal¹,

Kumar²

2023

Lecture Notes in Electrical Engineering

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A novel IMW-mrmr feature selection approach for early detection of hypertension with HRV analysis

Sohara Banu,

Nagaveni

2024

Int. j. inf. tecnol.

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HRV based feature selection for congestive heart failure and normal sinus rhythm for meticulous presaging of heart disease using machine learning

Cited by 7 publications

References 12 publications

Classification Model for Meticulous Presaging of Heart Disease Detection through SDA and NCA using Machine learning :CMSDANCA

Classification Model for Meticulous Presaging of Heart Disease Detection through SDA and NCA using Machine learning :CMSDANCA

Pre-eclampsia Risk Factors Association with Cardiovascular Disease Prediction and Diagnosing Using Machine Learning

A novel IMW-mrmr feature selection approach for early detection of hypertension with HRV analysis

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