File size: 3,624 Bytes
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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:
```python
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
)
|