Which algorithms are used in this lung cancer prediction project?

The system implements and compares two powerful ensemble algorithms: Random Forest and XGBoost. Random Forest achieved the best performance with 96.77% accuracy and an F1-score of 0.9666.

What features are used for lung cancer risk prediction?

The system uses 14 key risk factors including smoking, age, gender, yellow fingers, anxiety, peer pressure, chronic disease, fatigue, allergy, wheezing, alcohol consumption, coughing, shortness of breath, swallowing difficulty, and chest pain.

What is the accuracy of this lung cancer risk prediction model?

The Random Forest model achieves 96.77% test accuracy with precision of 96.84%, recall of 96.55%, F1-score of 0.9666, and ROC-AUC of 0.9962, making it highly reliable for clinical decision support.

Who can benefit from this lung cancer risk prediction system?

Healthcare providers can use it for early screening and risk stratification, primary care physicians for patient assessment, pulmonologists for referral prioritization, and patients for understanding their risk factors.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, XGBoost, imbalanced-learn (SMOTE), Matplotlib, and Seaborn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

Watch Demo Video

Machine Learning

Lung Cancer Risk Prediction Using Machine Learning

The Lung Cancer Risk Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts lung cancer risk using patient demographic and clinical data. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced ensemble algorithms like Random Forest and XGBoost with SMOTE to achieve 96.77% accuracy.

The system leverages 14 key risk factors including smoking status, age, gender, alcohol consumption, and various respiratory symptoms. It provides interactive visualizations, feature importance analysis, model comparison, and real-time lung cancer risk prediction to help healthcare professionals identify high-risk patients for early screening.

Python 3.8+ Machine Learning Random Forest XGBoost SMOTE Scikit-learn imbalanced-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Binary Cancer Risk Prediction
Random Forest (96.77% Accuracy)
XGBoost (95.16% Accuracy)
SMOTE for Class Imbalance
Leak-Free Cross-Validation
Feature Importance Analysis
Real-time Risk Prediction
Model Performance Comparison
5-Fold Stratified CV
Flask Web Application

Algorithms Used

🌲 Random Forest Classifier

Ensemble learning with 300 trees, handles non-linear relationships effectively

🎯 Accuracy: 96.77%

⚡ XGBoost Classifier

Optimized gradient boosting with 300 estimators, regularization prevents overfitting

🎯 Accuracy: 95.16%

🔄 SMOTE

Synthetic Minority Over-sampling for handling class imbalance

📊 k-neighbors: 5

🔍 5-Fold Leak-Free CV

Cross-validation with SMOTE inside each fold

📊 RF: 99.39% ± 0.56%

Methodology & Workflow

1 Data Loading & Inspection
Lung cancer survey dataset with 309 records

2 Data Preprocessing
Cleaning, encoding, feature scaling, outlier handling

3 Feature Engineering
Risk scores: smoking_risk, lifestyle_risk, respiratory_risk, total_risk_score

4 Model Training
Random Forest & XGBoost with SMOTE pipelines

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC, 5-fold CV

6 Web Deployment
Flask web app for real-time lung cancer risk prediction

Model Performance Comparison

Metric	Random Forest	XGBoost	Best
Accuracy	0.9677	0.9516	Random Forest
Precision	0.9684	0.9545	Random Forest
Recall	0.9655	0.9483	Random Forest
F1-Score	0.9666	0.9511	Random Forest
ROC-AUC	0.9962	0.9935	Random Forest
CV Mean (5-Fold)	0.9939	0.9913	Random Forest

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (309 records) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

Check Payment Status

LIMITED TIME OFFER -70%

Complete Project Package Lifetime Access

Original Price

₹9,999

Today's Price ₹2,999 💎 Save ₹7,000

You Save ₹7,000 (70% OFF)

Complete Source Code

Documentation & PPT

Video Tutorial

24/7 Expert Support

Scan & Pay with UPI

SECURE

Payee Thirumalai Kumar

UPI ID 9600095045@icici

Amount ₹2,999

Submit Your Payment

100% SECURE

Full Name *

Email *

Phone Number *

College/University *

Degree

Passout Year

Department

Payment Details

Enter your UPI Transaction ID and upload payment screenshot for verification.

Transaction ID *

Payment Screenshot *

Click to upload screenshot JPG, PNG, GIF, PDF • Max 5MB

📚 Academic & Research Support Services

Need help with Thesis, Dissertation, Assignments, or PhD Research? We've got you covered!

📝

Thesis & Dissertation

Complete thesis writing, research guidance, and formatting support

Expert Help

📄

Assignment Help

Quality assignment writing, editing, and proofreading services

100% Original

🔬

PhD Research

Research proposal, literature review, data analysis & publication

PhD Level

📊

Project Guidance

Academic projects, mini projects, and final year project support

Hands-on

📞 Need custom support? Contact us directly!

Chat on WhatsApp → Email Us

Final Year Projects