SCENARIO Recent public health data indicate a troubling increase in

SCENARIO Recent public health data indicate a troubling increase in kidney disease rates within specific suburban areas, attracting significant attention from public health practitioners. Determined to uncover the root causes and identify actionable risk factors to address this issue, the public health team has embarked on a comprehensive study. They have collected patient records and relevant information on medical factors and water quality, as provided in the dataset. Data Sample PatientID Age Gender BloodPressure BloodSugar Cholesterol BMI SmokingStatus ElectricConductivity pH DissolvedOxygen Turbidity TotalDissolvedSolids NitriteLevel NitrateLevel LeadConcentration ArsenicConcentration Humidity KidneyDisease TIW5219 120 Female 118 155.8 165 31.7 Former 336.2 7.4 9.57 1.44 455.4 0.165 1.97 0.0099 0.0063 48.7 0 There is more data this is just a sample Data Description: Variable Description PatientID Unique identifier of each patient Age Age of the individual Gender Gender of the individual BloodPressure Systolic blood pressure in mmHg BloodSugar Fasting blood sugar levels in mg/dL Cholesterol Total cholesterol level in mg/dL BodyMassIndex BMI, a measure of body fat based on height and weight SmokingStatus Smoking status of the individual [Never/ Former/ Current] ElectricConductivity Measurement of the water’s ability to conduct electricity, which can indicate contamination in μS/cm pH pH level of the water DissolvedOxygen Amount of oxygen dissolved in water in mg/L Turbidity Measure of water clarity in NTU TotalDissolvedSolids Measure of dissolved substances in water in mg/L NitriteLevel Nitrite concentration in water in mg/L NitrateLevel Nitrate concentration in water in mg/L LeadConcentration Lead concentration in water in mg/L ArsenicConcentration Arsenic concentration in water in mg/L Humidity Ambient humidity level in % KidneyDisease Presence or absence of kidney disease * Please note that this is a simulated data generated to resemble the real-world data for the purpose of this assignment. Consider the scenario described, the data set provided and your answers in Part A to answer the following questions. Build a logistic regression model incorporating polynomial terms. Clearly outline and explain each step of the process involved. [This question is designed to assess your critical thinking and analytical skills. Please note that guidance on how to complete the task will not be provided.] (8 Marks) Give the resultant accepted model (i.e. write the model equation) based on your findings above. Justify your answer clearly. (3 Marks) Use decision tree model to answer the research question. Clearly outline and explain each step of the process involved [Hint: model building, improvement and evaluation]. (12 Marks) Give the resultant model and interpret it. Clearly describe the terminal nodes [i.e. list the profiles]. [Include the relevant R output] (5 Marks) Compare the different resultant models (Part A Question 5, Part B Question 2 and Question 4) you obtained above. (12 Marks) Give the final accepted model based on your findings above and Part A. Justify your answer. (5 Marks) Apply an unsupervised learning technique of your choice to identify any interesting or hidden patterns in the dataset. Provide a clear explanation of the technique used and thoroughly describe your findings. (10 Marks) What are your conclusion and recommendations for this problem? [Hint: Use your results and findings from previous questions to answer this question] (5 Marks) — End of questions — APPENDIX [Attach all your R codes and outputs here.]

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