Classification of diabetic cardiomyopathy-related cells using machine learning
Özet
The patch-clamp technique is a significant tool in current electrophysiology research, especially
in cardiovascular diseases, because it can capture electrical activity of the heart from cardiomyocytes. It
is challenging to classify action potential waveforms in cardiological data from these recordings because
it relies largely on professional assistance. We discovered that supervised classification may be used to
predict the impact of electrophysiological perturbations on cardiomyopathic action potential groups in
rat ventricular cells. At the cellular level, action potential classifications are utilized to discern between
pathological and control waveforms in recorded cardiac action potentials. The four groups are as follows:
(1) control, (2) diabetes, (3) diabetes with angiotensin, and (4) angiotensin. The signal’s biologically
relevant features for the treatment of cardiomyopathy have been discovered. After they have been trained
with different sets of features, the results of the seven machine learning models are compared. The knearest neighbor approach, along with the decision tree and random forest algorithms, is the best classifier
for diagnosing aberrant action potential waveforms, with an accuracy of above 99% when compared to other
models. The high classification accuracy demonstrates that the gathered individual cardiac AP features
provide useful information regarding the pathological status of cardiomyocytes.
