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--- |
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license: mit |
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tags: |
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- sklearn |
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- classification |
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- healthcare |
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- lung-cancer |
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- streamlit |
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library_name: scikit-learn |
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model_name: Datathon Lung Cancer Detector |
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datasets: |
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- custom |
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language: en |
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--- |
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# 🫁 Datathon Lung Cancer Detector |
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This model predicts whether a patient is likely to have lung cancer based on clinical and behavioral risk factors. |
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It was trained on a dataset of 309 entries with 15 input features and a binary diagnosis label. |
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--- |
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## 📊 Input Features |
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| Feature | Type | Description | |
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|------------------------|----------|---------------------------------| |
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| `GENDER` | 0 = Female, 1 = Male | Biological sex | |
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| `AGE` | Integer | Patient age | |
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| `SMOKING` | 0/1 | Smoking habit | |
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| `YELLOW_FINGERS` | 0/1 | Stained fingers from smoking | |
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| `ANXIETY` | 0/1 | Anxiety symptoms | |
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| `PEER_PRESSURE` | 0/1 | Influence from peers | |
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| `CHRONIC DISEASE` | 0/1 | History of chronic illness | |
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| `FATIGUE` | 0/1 | Feeling of tiredness | |
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| `ALLERGY` | 0/1 | Known allergies | |
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| `WHEEZING` | 0/1 | Wheezing symptoms | |
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| `ALCOHOL CONSUMING` | 0/1 | Alcohol consumption | |
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| `COUGHING` | 0/1 | Persistent coughing | |
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| `SHORTNESS OF BREATH` | 0/1 | Difficulty breathing | |
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| `SWALLOWING DIFFICULTY`| 0/1 | Trouble swallowing | |
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| `CHEST PAIN` | 0/1 | Pain in chest area | |
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--- |
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## 🧠 Model Info |
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- **Algorithm**: XG Boost Classifier(Highest Score) |
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- **Framework**: Scikit-learn |
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- **Target**: `DIAGNOSIS_LUNG_CANCER` (`YES` = Lung Cancer, `NO` = No Cancer) |
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- **Dataset Size**: 309 samples |
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- **Preprocessing**: Label encoding, binary encoding for yes/no inputs |
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--- |
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## 🚀 Try It in Streamlit |
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This model is also available as a web app built using [Streamlit]. Access on https://datathonlungcancer.streamlit.app/ |
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```python |
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import streamlit as st |
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import pandas as pd |
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import joblib |
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model = joblib.load('model.pkl') |
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st.title('🫁 Lung Cancer Diagnosis') |
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st.write("Please fill out the following information to assess the likelihood of lung cancer.") |
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gender = st.selectbox('Gender', [0, 1], format_func=lambda x: "Female" if x == 0 else "Male") |
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age = st.number_input('Age', max_value=120, value=0) |
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smoking = st.selectbox('Smoking', ['Yes', 'No']) |
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yellow_fingers = st.selectbox('Yellow Fingers', ['Yes', 'No']) |
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anxiety = st.selectbox('Anxiety', ['Yes', 'No']) |
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peer_pressure = st.selectbox('Peer Pressure', ['Yes', 'No']) |
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chronic_disease = st.selectbox('Chronic Disease', ['Yes', 'No']) |
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fatigue = st.selectbox('Fatigue', ['Yes', 'No']) |
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allergy = st.selectbox('Allergy', ['Yes', 'No']) |
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wheezing = st.selectbox('Wheezing', ['Yes', 'No']) |
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alcohol = st.selectbox('Alcohol Consuming', ['Yes', 'No']) |
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coughing = st.selectbox('Coughing', ['Yes', 'No']) |
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shortness_of_breath = st.selectbox('Shortness of Breath', ['Yes', 'No']) |
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swallowing_difficulty = st.selectbox('Swallowing Difficulty', ['Yes', 'No']) |
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chest_pain = st.selectbox('Chest Pain', ['Yes', 'No']) |
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def binary_encode(value): |
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return 1 if value == 'Yes' else 0 |
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data = pd.DataFrame([[gender, age, |
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binary_encode(smoking), |
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binary_encode(yellow_fingers), |
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binary_encode(anxiety), |
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binary_encode(peer_pressure), |
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binary_encode(chronic_disease), |
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binary_encode(fatigue), |
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binary_encode(allergy), |
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binary_encode(wheezing), |
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binary_encode(alcohol), |
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binary_encode(coughing), |
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binary_encode(shortness_of_breath), |
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binary_encode(swallowing_difficulty), |
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binary_encode(chest_pain)]], |
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columns=['GENDER', 'AGE', 'SMOKING', 'YELLOW_FINGERS', 'ANXIETY', |
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'PEER_PRESSURE', 'CHRONIC DISEASE', 'FATIGUE', 'ALLERGY', |
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'WHEEZING', 'ALCOHOL CONSUMING', 'COUGHING', |
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'SHORTNESS OF BREATH', 'SWALLOWING DIFFICULTY', 'CHEST PAIN']) |
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if st.button('Predict'): |
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prediction = model.predict(data)[0] |
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if prediction == 1: |
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st.error("⚠️ High risk of lung cancer. Please consult a doctor.") |
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else: |
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st.success("✅ No Lung Cancer.") |
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