Using Mel-Frequency Cepstrum and Amplitude-Time Heart Variability as XGBoost Handcrafted Features for Heart Disease Detection

Author(s):  
SS Krivenko ◽  
AA Pulavskyi ◽  
LS Kryvenko ◽  
OV Krylova ◽  
SA Krivenko
Author(s):  
. Anika ◽  
Navpreet Kaur

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.


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