HRV analysis was done using recorded electrocardiographic signal from 104 LC participants and 30 control volunteers. Artificial neural network (ANN) and support vector machine (SVM) were implemented on HRV time-domain features for early prognosis of the disorder. Statistical significance of HRV parameters was tested, and graphical user interface (GUI) was also implemented.
Results:
It was revealed that progression of cancer causes low HRV. An accuracy of 89.64% and 100% was obtained with ANN and SVM, respectively, in automated cancer prediction. Statistical analysis suggested the significance of data at P < 0.05 between different performance statuses among patients.
Conclusion:
The severity of LC alters the sympathovagal balance through autonomic dysfunction. HRV analysis with an expert system was found useful for the early diagnosis of the disease, and thus, a noninvasive technique is of prognostic importance in classifying LC stages. The GUI designed for clinicians can help them to diagnose the Eastern Cooperative Oncology Group performance status scale of futurepatients.