README.md

metadata

dataset_info:
  features:
    - name: Age
      dtype: int64
    - name: Gender
      dtype: int64
    - name: Air_Pollution
      dtype: int64
    - name: Alcohol_use
      dtype: int64
    - name: Dust_Allergy
      dtype: int64
    - name: OccuPational_Hazards
      dtype: int64
    - name: Genetic_Risk
      dtype: int64
    - name: chronic_Lung_Disease
      dtype: int64
    - name: Balanced_Diet
      dtype: int64
    - name: Obesity
      dtype: int64
    - name: Smoking
      dtype: int64
    - name: Passive_Smoker
      dtype: int64
    - name: Chest_Pain
      dtype: int64
    - name: Coughing_of_Blood
      dtype: int64
    - name: Fatigue
      dtype: int64
    - name: Weight_Loss
      dtype: int64
    - name: Shortness_of_Breath
      dtype: int64
    - name: Wheezing
      dtype: int64
    - name: Swallowing_Difficulty
      dtype: int64
    - name: Clubbing_of_Finger_Nails
      dtype: int64
    - name: Frequent_Cold
      dtype: int64
    - name: Dry_Cough
      dtype: int64
    - name: Snoring
      dtype: int64
    - name: Level
      dtype:
        class_label:
          names:
            '0': Low
            '1': Medium
            '2': High
  splits:
    - name: train
      num_bytes: 192000
      num_examples: 1000
    - name: test
      num_bytes: 192000
      num_examples: 1000
  download_size: 40754
  dataset_size: 384000
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*

Lung Cancer Risk Classification Dataset

Purpose

This dataset is designed for building machine learning models to predict the risk level of lung cancer based on patient demographics, lifestyle, and medical history.
It can be used for research, model training, and educational purposes in healthcare AI applications.
The dataset contains 23 input features and one target label (Level) with three classes:

• Low (0)
• Medium (1)
• High (2)

---

Dataset Guide

Features

Feature Name	Type	Description
Age	int	Patient age
Gender	int	Gender encoded as 0/1
Air_Pollution	int	Exposure to air pollution
Alcohol_use	int	Alcohol consumption indicator
Dust_Allergy	int	Presence of dust allergy
OccuPational_Hazards	int	Occupational hazards exposure
Genetic_Risk	int	Genetic risk factor
chronic_Lung_Disease	int	History of chronic lung disease
Balanced_Diet	int	Balanced diet indicator
Obesity	int	Obesity indicator
Smoking	int	Smoking habit indicator
Passive_Smoker	int	Exposure to passive smoking
Chest_Pain	int	Chest pain indicator
Coughing_of_Blood	int	Presence of blood in cough
Fatigue	int	Fatigue indicator
Weight_Loss	int	Weight loss indicator
Shortness_of_Breath	int	Shortness of breath indicator
Wheezing	int	Wheezing indicator
Swallowing_Difficulty	int	Difficulty swallowing indicator
Clubbing_of_Finger_Nails	int	Clubbing of finger nails indicator
Frequent_Cold	int	Frequency of colds
Dry_Cough	int	Dry cough indicator
Snoring	int	Snoring indicator
Level	class	Target label: 0=Low, 1=Medium, 2=High

Recommended Usage

1. Load the dataset using the datasets library:

from datasets import load_dataset

dataset = load_dataset("edcelbogs/lung-cancer-classification")



### Prepare for training in TensorFlow or PyTorch:

#Example Convert to TensorFlow dataset:

tf_dataset = dataset["train"].to_tf_dataset(
    columns=[c for c in dataset["train"].column_names if c != "Level"],
    label_cols="Level",
    shuffle=True,
    batch_size=32
)