## 4 Discussion  
In the present study, we highlighted the major risk factors of stroke obtained from the statistical analysis of the baseline demographics and clinical characteristics of the study population. We demonstrated the use of a machine-learning algorithms and developed a classification model with highly ranked ECG features obtained from various datasets, such as the HRV parameters of the time and spectral domains, statistical and impulsive metric variables and ECG intervals. [PERSON] et al. reported the higher mean age and overrepresentation of male sex in the baseline characteristics of stroke patients [39]. In a study conducted by [PERSON] et al., the number of males found to be higher consisted of 62.9% among the overall stroke patients [40]. Similarly, our findings noticed that the number of male stroke subjects accounted were higher and comprised 63% more than female stroke subjects. Some of the major risk factors for stroke are reported as age, hypertension, diabetes, dyslipidemia, cardiovascular diseases and smoking and alcohol consumption [41]. Our study disclosed the most significant risk factors for stroke as age, gender and dyslipidemia (\(p<0.05\)), and thereby, people with one or more of these risk factors are considered to be at high risk of stroke. Besides the demographics and clinical data, we explored the HRV indices of time and frequency domains to find the autonomic dysfunction of the stroke subjects. Acute stroke generally affects the autonomic nervous system, and the reduction in HRV is a regular sign of illness in stroke patients [42]. Several studies also reported that a lower heart rate variability was associated with a higher risk of incident stroke [43; 44; 45; 46]. Correspondingly, our results noticed a lowered value of RMSSD, SDSD and pNN50 of the time domain parameters and lowered LF, HF and VLF of the frequency domain parameters for the stroke subjects, indicating the relation of lower heart rate variability with stroke. Furthermore, we observed that the stroke patients had elevated values of the LF/HF ratio, similar to the findings of [PERSON] et al. and [PERSON] et al., which revealed that the patients with acute strokes had increased LF/HF values when compared to the normal people [47; 48]. However, no significant differences were observed in the higher order statistical and impulsive metric variables between the groups. The prevalence of ECG changes have a significant relationship with aging [49]; additionally, the ECG abnormalities are regularly seen in stroke patients [50; 51]. Many studies reported that the most common ECG abnormalities associated with acute stroke are arrhythmia, prolonged QTc and T wave and U wave abnormalities [19; 52; 53; 54; 55]. Accordingly, we noticed the prolonged QTc in the stroke group, including both male and female subjects, with the overall mean value of the QTc greater than 450 milliseconds. A similar trend also seen in the control group might \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Learning Method** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline KNN & 96.6\% & 94.3\% & 99.1\% & 96.6\% \\ Random forest & 94.4\% & 91.7\% & 97.7\% & 94.6\% \\ SVM & 85.4\% & 81.5\% & 91.7\% & 86.3\% \\ Naive Bayes & 72.7\% & 64.2\% & 87.8\% & 74.1\% \\ Logistic regression & 66.9\% & 57.1\% & 91.7\% & 70.3\% \\ \hline \hline \end{tabular}  
* KNN: k-nearest neighbor and SVM: support vector machine.  
\end{table} 
Table 5: Results of the classification performance of the models. because of the aging effects [56]. In addition to that, we identified a slightly prolonged QRS duration, with the mean value of the QRS greater than 110 ms in the stroke group. However, a significant difference was detected only in the QT and ST intervals between the study populations. Thus, our study performed an extensive analysis on the extracted features and also noted significant changes.