Which algorithms are used in this medical appointment no-show prediction project?

The system implements and compares two algorithms: Logistic Regression (interpretable) and Random Forest (ensemble). Random Forest achieved superior performance with 82.5% accuracy and 0.871 ROC-AUC.

What is the accuracy of the no-show prediction model?

The Random Forest model achieves 82.5% accuracy with an F1-score of 0.824 and ROC-AUC of 0.871, demonstrating strong predictive capability for healthcare appointment attendance.

Who can benefit from this no-show prediction system?

Healthcare providers, hospitals, clinics, healthcare administrators, and patient engagement teams can use this system for resource optimization, patient outreach, and operational efficiency.

What technologies are used to build this project?

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

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Machine Learning

Medical Appointment No-Show Prediction Using Machine Learning

The Medical Appointment No-Show Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts whether a patient will miss their medical appointment. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Logistic Regression and Random Forest with leak-free SMOTE, achieving 82.5% accuracy and 0.871 ROC-AUC.

The system utilizes the Kaggle Medical Appointment No-Show dataset containing 110,527 appointment records with 14 features including patient demographics, appointment details, and health conditions. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time no-show prediction, enabling healthcare providers to identify high-risk patients and implement targeted interventions to reduce no-show rates by 20-30%.

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

Key Features:

Multi-Model No-Show Prediction
Random Forest (82.5% Accuracy)
Logistic Regression (79.4% Accuracy)
Feature Engineering (health_risk_score, wait_days)
Leak-free SMOTE Cross-Validation
Interactive Visualizations
Real-time No-Show Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

📊 Logistic Regression

Linear classifier with L2 regularization, class_weight='balanced', max_iter=1000

🎯 Accuracy: 79.4%

🌲 Random Forest

Ensemble learning with n_estimators=100, max_depth=10, class_weight='balanced'

🎯 Accuracy: 82.5%

🔧 Feature Engineering

days_until_appointment, health_risk_score, age_category, scheduled_hour

📊 Top Feature: Wait Days

🔄 SMOTE Oversampling

Synthetic minority oversampling applied leak-free inside CV folds

📊 CV Mean: 86.4%

Methodology & Workflow

1 Data Collection
110,527 appointment records with 14 features

2 Data Preprocessing
Cleaning, encoding, feature engineering, scaling

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
Logistic Regression and Random Forest with leak-free SMOTE

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

6 Web Deployment
Flask web app with real-time no-show prediction

Model Performance Comparison

Metric	Logistic Regression	Random Forest	Best
Test Accuracy	79.4%	82.5%	Random Forest
Test Precision	79.1%	82.3%	Random Forest
Test Recall	79.4%	82.5%	Random Forest
Test F1-Score	79.2%	82.4%	Random Forest
Test ROC-AUC	83.1%	87.1%	Random Forest
CV Mean ROC-AUC	82.5%	86.4%	Random Forest

Project Package Includes:

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

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