Record Details

<p class="IOPTitle">Implementation of machine learning model-based decision support system for healthcare professionals to predict T2DM risk using heart rate variability features</p>

Online Publishing @ NISCAIR

View Archive Info
 
 
Field Value
 
Authentication Code dc
 
Title Statement <p class="IOPTitle">Implementation of machine learning model-based decision support system for healthcare professionals to predict T2DM risk using heart rate variability features</p>
 
Added Entry - Uncontrolled Name Rathod, Shashikant Rajaram; College of Engineering, Pune 411 005, India
Chaskar, Uttam ; College of Engineering, Pune 411 005, India
Phadke, Leena ; Smt. Kashibai Navale Medical College and General Hospital, Pune 411 041, India
Patil, Chetan Kumar; College of Engineering, Pune 411 005, India
NA
 
Uncontrolled Index Term Instrumentation
Heart rate variability, Type 2 diabetes mellitus, Gradient boosting decision tree, Extreme gradient boosting, Categorical boosting
 
Summary, etc. <p style="text-align: justify;">Non-invasive early diabetes prediction has been gaining much premarkable over the last decade. Heart rate variability (HRV) is the only non-invasive technique that can predict the future occurrence of the disease. Early prediction of diabetes can help doctors start an early intervention. To this end, the authors have developed a computational machine learning model to predict type 2 diabetes mellitus (T2DM) risk using heart rate variability features and have evaluated its robustness against the HRV of 50 patients data. The electrocardiogram (ECG) signal of the control population (n=40) and T2DM population (n=120) have been recorded in the supine position for 5 minutes, and HRV signals have been obtained. The time domain, frequency domain, and non-linear features have been extracted from the HRV signal. A decision support system has been developed based on a machine learning algorithm. Finally, the decision support system has been validated using the HRV features of 50 patients (Control n=10 and T2DM n=40). HRV features are selected for the prediction of T2DM. The decision support system has been designed using three machine learning models: Gradient boosting decision tree (GBDT), Extreme Gradient boosting (XGBoost), Categorical boosting (CatBoost), and their performance have been evaluated based on the Accuracy (ACC), Sensitivity (SEN), Specificity (SPC), Positive predicted value (PPV), Negative predicted value (NPV), False-positive rate (FPR), False-negative rate (FNR), F1 score, and Area under the receiver operating characteristic curve (AUC) metrics. The CatBoost model offers the best performance outcomes, and its results have been validated on 50 patients. Thus the CatBoost model can be use as a decision support system in hospitals to predict the risk of T2DM.</p>
 
Publication, Distribution, Etc. Indian Journal of Engineering and Materials Sciences (IJEMS)
2021-09-10 13:39:03
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/IJEMS/article/view/46773
 
Data Source Entry Indian Journal of Engineering and Materials Sciences (IJEMS); ##issue.vol## 28, ##issue.no## 3 (2021): IJEMS-June 2021
 
Language Note en
 
Terms Governing Use and Reproduction Note Except where otherwise noted, the Articles on this site are licensed under Creative Commons License: CC Attribution-Noncommercial-No Derivative Works 2.5 India © 2015. The Council of Scientific &amp; Industrial Research, New Delhi.