🏥 Problem: Patient Status Analysis and Prediction

For this problem you need to implement a machine learning model to predict the value of the Status field using an available dataset that contains information about patients. The dataset is organized in a CSV file that includes various features, and the model evaluation will be based on precision for the Dead class.

📊 Dataset

The dataset contains the following fields:

• Age: Patient's age.
• Race: Patient's race (e.g., White, Other).
• Marital Status: Patient's marital status.
• T Stage: Tumor stage (T1, T2, T3, T4).
• N Stage: Degree of lymph node involvement.
• 6th Stage: Cancer stage according to the 6th edition TNM classification.
• differentiate: Degree of tumor differentiation.
• Grade: Histological grade of the tumor (1, 2, 3 etc.).
• A Stage: Disease stage classification.
• Tumor Size: Tumor size.
• BMI: Body mass index.
• Heart Rate: Heart rate.
• Serum Creatinine: Serum creatinine level.
• Uric Acid: Uric acid level.
• Hemoglobin: Hemoglobin concentration.
• GFR (Glomerular Filtration Rate): Glomerular filtration rate.
• Serum Sodium: Serum sodium concentration.
• Serum Potassium: Serum potassium concentration.
• Serum Albumin: Serum albumin level.
• Lactate: Lactate concentration.
• Status: Patient status ("Dead" or "Alive") – target field.

📝 Tasks

For the first subtasks, you will need to load the dataset and perform statistical analyses on train.csv.

Subtask 1 (10 points)

Classify kidney function for each patient based on the GFR value:

• Normal if GFR >= 90
• Mildly Decreased if 60 <= GFR < 90

Subtask 2 (10 points)

Calculate the quartiles of values from the Serum Creatinine column (Q1, Q2, Q3) and classify patients in test:

• Very Low if Serum Creatinine <= Q1
• Low if Q1 < Serum Creatinine <= Q2
• High if Q2 < Serum Creatinine <= Q3
• Very High if Serum Creatinine > Q3

Subtask 3 (10 points)

Determining BMI value:

• 1 if BMI > median from train
• 0 if BMI <= median

Subtask 4 (10 points)

Number of patients from train who are in the same T Stage as the patient from test.

Subtask 5 (60 points)

Build an ML model to predict Status (Dead or Alive) based on features. Evaluation is based on precision for the Dead class.

🧮 Evaluation

The evaluation metric is precision for the Dead class:

where:

• TP = number of patients correctly predicted as Dead
• FP = number of patients incorrectly predicted as Dead

Submission file format

The submission file must be in CSV (Comma-Separated Values) format, with the following columns:

• subtaskID - the task index for which we provide the answer
• datapointID - unique identifier of the patient from the dataset
• answer - the answer associated with the patient from the dataset for the indicated task

Example:

subtaskID,datapointID,answer
1,3220,Normal
2,3220,Very High
3,3220,0
4,3220,1281
5,3220,1

📌 Notes

• The target field is Status.
• Some fields may contain missing values (NaN) and must be handled.
• It is recommended to analyze the correlation between variables and the target variable.
• Detect and remove outliers if they affect model performance.
• Submitting a sample_output for local testing provides 5 points.

🗂️ Useful resources

• Complete Starter Kit – contains a skeleton from which you can start solving the problem