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Your team has conducted a clinical trial aimed at improving…

Your team has conducted a clinical trial aimed at improving the accuracy of diabetes diagnoses. One of the approaches you tested relies on patients’ self-reported conditions during hospital visits, as well as their ages, to identify whether they have diabetes. The clinical trial involved 196 hospital patients between the ages of 40 and 85. Among these participants: 116 were identified as healthy or pre-diabetic (label 0).80 were diagnosed with diabetes (label 1). Patients were asked to report whether they experienced 19 specific conditions, such as high blood pressure, kidney problems, or digestive issues. For each condition, the response is coded as follows: 1 if the patient reported the condition,0 if the patient did not report the condition. As a result, the dataset includes 19 binary features (representing the reported conditions), along with the patients’ ages (ranging from 40 to 85). These features serve as inputs for a supervised learning model to predict diabetes status. Now, your analysts are seeking your advice on the following questions: 1. Data Preprocessing: Are there any preprocessing steps that should be performed before applying the machine learning model? Provide your rationale. (2pt) 2. Model Evaluation: Based on your recommendation, the team applied a gradient boosting algorithm and obtained the following results on a testing set of 40 patients: Accuracy: 0.75Confusion Matrix:[[18  6] [ 4 12]] Explain the meaning of each number and evaluate the model’s performance within the context of this problem, considering both the accuracy and the confusion matrix. (5pt)