https://github.com/angchekar28/pcos-machine-learning-analysis
This project evaluates various machine learning models for diagnosing Polycystic Ovary Syndrome (PCOS) based on medical and clinical features. It compares models like Decision Tree, XGBoost, Random Forest, SVM, and Logistic Regression, analyzing their accuracy and execution time to determine the best-performing model for PCOS prediction.
https://github.com/angchekar28/pcos-machine-learning-analysis
classification data-science machine-learning pcos-detection python
Last synced: about 2 months ago
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This project evaluates various machine learning models for diagnosing Polycystic Ovary Syndrome (PCOS) based on medical and clinical features. It compares models like Decision Tree, XGBoost, Random Forest, SVM, and Logistic Regression, analyzing their accuracy and execution time to determine the best-performing model for PCOS prediction.
- Host: GitHub
- URL: https://github.com/angchekar28/pcos-machine-learning-analysis
- Owner: angchekar28
- License: mit
- Created: 2025-03-01T09:10:41.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-01T09:15:51.000Z (over 1 year ago)
- Last Synced: 2025-03-01T10:23:15.068Z (over 1 year ago)
- Topics: classification, data-science, machine-learning, pcos-detection, python
- Language: Jupyter Notebook
- Homepage:
- Size: 550 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🔍 PCOS Diagnosis: Machine Learning Model Comparison
## 📌 Overview
This project evaluates various **machine learning models** for diagnosing **Polycystic Ovary Syndrome (PCOS)** based on medical and clinical features. The goal is to determine the most **accurate and efficient** model for predicting PCOS while analyzing execution time and classification metrics.
## 📊 Dataset
The dataset contains medical records of individuals, including various physiological and lifestyle-related parameters that may influence PCOS diagnosis.
### Features:
- **Independent Variables:** Age, BMI, Insulin levels, LH/FSH ratio, Follicle count, etc.
- **Target Variable:** Presence or absence of PCOS (Binary: 1 = PCOS, 0 = No PCOS)
## 🛠️ Technologies Used
- **Python** (pandas, numpy, scikit-learn, XGBoost)
- **Jupyter Notebook/Kaggle/Colab**
- **Matplotlib & Seaborn** for visualization
## 🚀 Models Implemented
The following classification models were evaluated:
1. **Random Forest**
2. **Logistic Regression**
3. **Support Vector Machine (RBF & Linear)**
4. **K-Nearest Neighbors**
5. **Decision Tree**
6. **XGBoost**
7. **Naive Bayes**
## 📈 Model Performance Comparison
| Model | Accuracy | Precision (Class 1) | Recall (Class 1) | F1-Score (Class 1) | Implementation Time (s) |
|-------------------------------|----------|---------------------|------------------|-------------------|--------------------------|
| Random Forest | 99.00% | 0.99 | 0.95 | 0.97 | 0.1783 |
| Logistic Regression | 88.50% | 0.72 | 0.67 | 0.69 | 0.0059 |
| SVM (RBF Kernel) | 96.00% | 0.90 | 0.90 | 0.90 | 0.0089 |
| SVM (Linear Kernel) | 89.50% | 0.74 | 0.72 | 0.73 | 0.0100 |
| K-Nearest Neighbors | 96.50% | 0.92 | 0.90 | 0.91 | 0.0132 |
| Decision Tree | 99.50% | 1.00 | 0.97 | 0.99 | 0.0026 |
| XGBoost | 99.50% | 1.00 | 0.97 | 0.99 | 0.0332 |
| Naive Bayes | 69.50% | 0.39 | 1.00 | 0.56 | 0.0019 |
## 🏆 Conclusion
### ✅ Best Model Choices:
1. **Decision Tree & XGBoost** – Highest accuracy (99.50%) with strong precision, recall, and F1-scores. Decision Tree was the fastest among them (0.0026s).
2. **Random Forest** – Excellent accuracy (99%) but had the longest execution time (0.1783s).
3. **SVM (RBF) & KNN** – Reliable models with **96%+ accuracy**, but KNN took slightly more time (0.0132s).
### ⚡ Performance vs. Speed:
- **Fastest Model:** Naive Bayes (0.0019s), but it had the lowest accuracy (69.50%).
- **Balanced Performance:** XGBoost and Decision Tree provide the best trade-off between accuracy and execution time.
### ❌ Weak Model Choices:
- **Naive Bayes** struggled significantly due to **class imbalance**, making it unsuitable.
- **Logistic Regression & Linear SVM** had moderate performance but **struggled with Class 1 predictions**.
### 🔥 Final Recommendation:
For **high accuracy and efficiency**, **Decision Tree or XGBoost** are the best choices. If **speed is not a constraint**, **Random Forest** is also an excellent option.
## 🔧 Installation & Usage
### 1️⃣ Clone the Repository
```bash
git clone https://github.com/yourusername/PCOS-ML-Model-Comparison.git
cd PCOS-ML-Model-Comparison
```
### 2️⃣ Install Dependencies
```bash
pip install -r requirements.txt
```
### 3️⃣ Run the Jupyter Notebook
```bash
jupyter notebook