Smoker Status Prediction — Web Interface Manual

This web application allows users to predict smoking status (Smoker or Non-Smoker) based on biometric and clinical data input. The prediction is powered by machine learning models trained on health datasets. You can visit the online demo for a quick review.

Table of Contents

1. Input Form (No. 1)
2. Model Selector (No. 2)
3. Submit Button (No. 3)
4. Clear Button (No. 4)
5. Fill Sample Button (No. 5)
6. Prediction Result (No. 6)
7. Reference Info (No. 7)

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1. Input Form

Location: Top section of the page

Enter biometric and medical data of the subject/patient.
These include:

• Age (in 5-year intervals)
• Height (cm)
• Weight (kg)
• Waist circumference (cm)
• Eyesight (Left & Right)
• Hearing (Left & Right)
• Blood pressure (Systolic & Diastolic)
• Fasting blood sugar (mg/dL)
• Cholesterol (Total, HDL, LDL, Triglyceride)
• Hemoglobin (g/dL)
• Urine protein level (0–4)
• Serum creatinine (mg/dL)
• Liver enzymes (AST, ALT, GTP)
• Dental caries (0 = no, 1 = yes)

Refer to the reference section below for valid value ranges.

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2. Model Selector

Location: Dropdown below the form

Choose a trained machine learning model to perform prediction.
Available options include models like:

• Logistic Regression
• Random Forest
• XGBoost

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3. Submit Button

Label: Submit

Click this to send the input data to the selected ML model.
The model will return a prediction of whether the subject is a Smoker or Non-Smoker.

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4. Clear Button

Label: Clear

This button will clear all fields in the form.
Useful if you want to reset the form before entering new data.

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5. Fill Sample Button

Label: Fill with Example

Click this to autofill the form with a random sample from the dataset.
This is helpful for testing the app or demoing predictions.
Note: It will exclude the actual smoking status during prediction.

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6. Prediction Result

Location: Below the Submit button

• If the model predicts Non-Smoker, the result box will appear green.
• If the model predicts Smoker, it will appear red.

Use this prediction to guide further analysis or decision-making.

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7. Reference Info

Located: At the bottom of the page

Provides:

• Dataset origin: Binary Prediction of Smoker Status using Bio-Signals | Kaggle
• Field descriptions: What each input represents and how it relates to smoking status

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Notes

• Target: The goal is to predict the smoking status (0 = Non-Smoker, 1 = Smoker)
• Outlier Handling: Backend has safeguards for invalid inputs

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Created as part of the Smoker Status Prediction Project — Machine Learning Batch 7