Diabetes Diagnosis Based on Blood Tests

🩺 Diabetes Diagnosis Based on Blood Tests

Problem Description

The goal is to build a classification model that predicts whether a patient has diabetes based on blood tests and demographic data.

Each patient is characterized by 8 numerical attributes obtained from analyses and clinical measurements, and the label (target) indicates the presence of diabetes (1 for positive, 0 for negative).

This type of problem belongs to the binary classification category.

🔹 Features

• pregnancies – number of pregnancies
• glucose – blood glucose level
• blood_pressure – blood pressure
• skin_thickness – skin fold thickness
• insulin – insulin level
• bmi – body mass index
• diabetes_pedigree_function – genetic risk score
• age – patient age

📘 Input File Structure

train.csv

Contains all 8 feature columns plus the column:

• target – indicates the presence of diabetes (0 or 1)

Example:

test.csv

Contains the same columns without target, but includes SampleID.

Example:

📤 Submission

The output file (submission.csv) must contain exactly two columns:

• SampleID
• label – the label predicted by the model (0 or 1)

Example:

⚙️ Evaluation

Model evaluation will be performed using the following metric:

• Binary F1-score

This metric is suitable for binary classification because it gives equal importance to prediction accuracy for both classes.

General formula:

where:

The final score is expressed as a percentage (0–100), rounded to two decimal places.

📊 Source

The dataset comes from the original collection:
Pima Indians Diabetes Database – Kaggle