k-NN binary classification of heart failures using myocardial current density distribution maps

Udovychenko, Yevhenii; Попов, Антон; Chaikovsky, Illya

doi:10.1109/sps.2015.7168283

Cited by 9 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, instant magnetic field distribution maps are built using two-dimensional interpolation algorithms and based on synchronous averaged MCG curves. Then, the MCG inverse problem is solved, and the map of magnetic field distribution can be converted into an instant CDDM ( 12 ).…”

Section: Data Collection and Methods Descriptionmentioning

confidence: 99%

Multistage Classification of Current Density Distribution Maps of Various Heart States Based on Correlation Analysis and k-NN Algorithm

Udovychenko

Попов

Chaikovsky

2021

Front. Med. Technol.

Self Cite

View full text Add to dashboard Cite

Magnetocardiography is a modern method of registration of the magnetic component of electromagnetic field, generated by heart activity. Magnetocardiography results are a useful source for the diagnosis of various heart diseases and states, but their usage is still undervalued in the cardiology community. In this study, a two-stage classification by correlation analysis using a k-Nearest Neighbor (k-NN) algorithm is applied for the binary classification of myocardium current density distribution maps (CDDMs). Fourteen groups of CDDMs from patients with different heart states, healthy volunteers, sportsmen, patients with negative T-peak, patients with myocardial damage, male and female patients with microvascular disease, patients with ischemic heart disease, and patients with left ventricular hypertrophy, divided into five and three different groups depending on the degree of pathology, were compared. Selection of best metric, used in classifier and number of neighbors, was performed to define the classifier with best performance for each pair of heart states. Accuracy, specificity, sensitivity, and precision values dependent on the number of neighbors are obtained for each class. The proposed method allows to obtain a value of average accuracy equal to 96%, 70% sensitivity, 98% specificity, and 70% precision.

show abstract

Section: Data Collection and Methods Descriptionmentioning

confidence: 99%

Multistage Classification of Current Density Distribution Maps of Various Heart States Based on Correlation Analysis and k-NN Algorithm

Udovychenko

Попов

Chaikovsky

2021

Front. Med. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Figure 4 shows the dependence of accuracy on number of neighbors for different metrics for group "Negative_T". As we can see, using a Cityblock metric gives the best result for almost all numbers of neighbors, showing the value of accuracy higher up to 4% comparing to Eucledian metric, used in our previous study [14]. It gives an opportunity to obtain accuracy of classification up to 90%.…”

Section: B Dependence Of Classification Results On Different Metricsmentioning

confidence: 54%

“…This paper is an extended version of paper "k-NN Binary Classification of Heart Failures Using Myocardial Current Density Distribution Maps" [14], presented in Signal Processing Symposium, Debe, Poland, June 10 -12, 2015. Y. Udovychenko, A. Popov are with the Physical and Biomedical Electronics Departmentof National Technical University of Ukraine"Kyiv Polytechnic Institute",Kyiv, Ukraine, (email: yevhenii.udovychenko@yahoo.gr,popov.kpi@gmail.com).…”

Section: One Of the Approaches Of Studying Magnetic Field In Humanmentioning

confidence: 99%

“…It was also shown in the same study that k-NN classifier with correctly chosen distance metric shows better results even comparing to Support Vector Machines used for the same classification tasks. The result of In our previous study [14] we used k-NN classifier with Euclidian metric. In this study we tried to improve classification results by employing other available metrics: Mahalanobis, Cityblock and Chebychev to the task of classification between CDDMs containing heart failures and without them.…”

Section: K-nearest Neighbor Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Binary Classification of Heart Failures Using k-NN with Various Distance Metrics

Udovychenko¹,

Попов²,

Chaikovsky³

2015

International Journal of Electronics and Telecommunications

Self Cite

View full text Add to dashboard Cite

Magnetocardiography is a sensitive technique of measuring low magnetic fields generated by heart functioning, which is used for diagnostics of large number of cardiovascular diseases. In this paper, k-nearest neighbor (k-NN)technique is used for binary classification of myocardium current density distribution maps (CDDM)from patients with negative T-peak, male and female patients with microvessels (diffuse) abnormalities and sportsmen, which are compared with normal control subjects. Number of neighbors for k-NN classifier was selected to obtain highest classification characteristics. Specificity, accuracy, precision and sensitivity of classification as functions of number of neighbors in k-NN are obtained for classification with several distance measures: Mahalanobis, Cityblock, Eucleadian and Chebyshev. Increase of the accuracy of classification for all groups up to 10% was obtained usingCityblock distance metric in binary k-NN classifier with 19 - 27 neighbors, comparing to other metrics. Obtained results are acceptable for further patient’s state evaluation.

show abstract

“…Udovychenko et al, [20] classifies heart failure by using K-NN Binary. From the experiment results obtained 80-88% accuracy range, 70-95% sensitivity, 78-95% specification and 77-93% precision.…”

Section: Literature Studymentioning

confidence: 99%

Performance Analysis of Data Mining Methods for Sexually Transmitted Disease Classification

Yuliastuti¹,

Alfiyatin²,

Rizki³

et al. 2018

IJECE

View full text Add to dashboard Cite

According to health reports of Malang city, many people are exposed to sexually transmitted diseases and most sufferers are not aware of the symptoms. Malang city being known as a city of education so that every year the population number increases, it is at risk of increasing the spread of sexually transmitted diseases virus. This problem is important to be solved to treat earlier sufferers sexually transmitted diseases virus in order to reduce the burden of patient spending. In this research, authors conduct data mining methods to classifying sexually transmitted diseases. From the experiment result shows that K-NN is the best method for solve this problem with 90% accuracy.

show abstract

k-NN binary classification of heart failures using myocardial current density distribution maps

Cited by 9 publications

References 9 publications

Multistage Classification of Current Density Distribution Maps of Various Heart States Based on Correlation Analysis and k-NN Algorithm

Multistage Classification of Current Density Distribution Maps of Various Heart States Based on Correlation Analysis and k-NN Algorithm

Binary Classification of Heart Failures Using k-NN with Various Distance Metrics

Performance Analysis of Data Mining Methods for Sexually Transmitted Disease Classification

Contact Info

Product

Resources

About