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Logistic Regression is a statistical model used for identifying the probability of a class with the help of binary dependent variables i.e. Yes or No. It indicates whether a class belongs to the Yes category or the No category. For example, after executing an event on an object the results maybe Win or Loss, Pass or Fail, Accept or Not-Accept, etc. The mathematical representation of the Logistic Regression model is done by two indicator variables i.e. 0 and 1. It is different from the Linear Regression technique as depicted in Ref. [54]. As logistic regression has its importance in the real-life classification problems as depicted in Refs. [55, 56], different fields like Medical Sciences, Social Sciences, ML are using this model in their various field of operations.

The Logistic Regression is performed on the heart disease dataset [41]. The Receiver Operating Characteristics (ROC) is calculated that is based on the true positive rate that is plotted on the y-axis and the false positive rate that is plotted on the x-axis. After performing the logistic regression in python (Google Colab), the outcome is represented in Figure 1.11 and Table 1.2. Figure 1.11 represents the ROC curve and Table 1.2 represents the Area under the ROC Curve (AUC).

At the time of processing, the AUC value obtained (Table 1.2) on training data is 0.8374022, but when the data is processed for testing then the obtained result is outstanding (i.e. 0.9409523). This indicates that the model is more than 90% efficient for classification. In the next section, the difference between Linear and Logistic Regression is discussed.

Figure 1.11 ROC curve for logistic regression.

Table 1.2 AUC: Logistic regression.

Parameter	Data	Value	Result
The area under	Training Data	0.8374022	Excellent
the ROC Curve (AUC)	Test Data	0.9409523	Outstanding
	Index: 0.5: No Discriminant, 0.6–0.8: Can be considered accepted, 0.8–0.9: Excellent, >0.9: Outstanding