ADS Capstone Chronicles Revised

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Figure 9 Histograms and Distribution of Numeric and Ordinal Features

Figure 10 recognizes a similar imbalance among the binary features in the patient data frame. 11 of the 14 binary features demonstrate a significant class imbalance, with the majority class being overrepresented. To mitigate the impact of the unbalanced binary variables, the data frame will have to be resampled during preprocessing to ensure a balanced

representation of each class. It is important to note that the sex feature is the most balanced binary feature, with approximately equal representation of male and female patients. This equal balance suggests that the patient data are adhering to ethical guidelines and avoiding demographic biases; a necessity in the modeling of health-related data.

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