Predicting PCOS Using Machine Learning Algorithms: A Binary Classification Approach

Introduction

Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder among women of reproductive age, affecting approximately 1 in 10 women worldwide. Early detection and diagnosis of PCOS are crucial for effective management and prevention of associated health risks such as infertility, diabetes, and cardiovascular disease. In recent years, machine learning algorithms have emerged as powerful tools for predictive healthcare analytics. In this article, we’ll explore how machine learning can be employed for the early detection of PCOS through binary classification.

Understanding PCOS

PCOS is characterized by a combination of symptoms, including irregular menstrual cycles, excessive androgen levels, and polycystic ovaries. However, diagnosing PCOS can be challenging due to variations in symptoms and the absence of a single definitive test. Traditional diagnostic criteria include the Rotterdam criteria, which require the presence of at least two out of three key features: irregular periods, elevated androgen levels, and polycystic ovaries observed via ultrasound.

Machine Learning for PCOS Prediction

Machine learning techniques offer a data-driven approach to predict PCOS based on features extracted from patient data. These features may include hormonal levels, menstrual cycle regularity, BMI, and other clinical parameters. By training machine learning models on historical patient data with known PCOS status, we can develop predictive models capable of classifying new patients as either having PCOS or not.

Binary Classification Algorithms

Several machine learning algorithms are well-suited for binary classification tasks like predicting PCOS. Some commonly used algorithms include:

• Logistic Regression:
  Logistic Regression is a binary classification algorithm used to predict the probability of a categorical outcome (e.g., PCOS or non-PCOS) based on one or more independent variables (features). It models the relationship between the independent variables and the binary outcome using the logistic function.
• Advantages of Logistic Regression:

1. Simple and interpretable: Easy to understand and explain results.
2. Efficient for small datasets: Works well even with limited data.
3. Outputs probabilities: Provides probability scores for predictions.

• Limitations of Logistic Regression:

1. Assumes linear relationship: Limited in capturing complex nonlinear patterns.
2. Sensitive to outliers: Outliers can significantly impact model performance.
3. Cannot handle multicollinearity well: Correlated features may affect model stability.

• Performance on PCOS Prediction: Logistic Regression can perform well for PCOS prediction tasks, especially with well-preprocessed and relevant features. However, its performance may be limited by the complexity of underlying relationships and the presence of highly correlated features in the dataset.
• Random Forest Algorithm: Random Forest is an ensemble learning method that constructs multiple decision trees during training. It randomly selects subsets of features and samples from the dataset to build each tree. During prediction, it aggregates the results of individual trees to make the final prediction.
• Advantages of Random Forest:

1. Robustness to overfitting: Combining multiple trees reduces overfitting compared to a single decision tree.
2. Handles large datasets: Random Forest can handle large datasets with high dimensionality efficiently.
3. Feature importance: It provides insights into feature importance, helping in feature selection and interpretation.

• Limitations of Random Forest:

1. Complexity: Random Forest can be computationally intensive and complex due to the multiple trees it builds.
2. Interpretability: The model’s ensemble nature can make it less interpretable compared to simpler models like decision trees.
3. Memory consumption: It may require more memory compared to simpler models, especially for large datasets.

• Performance on PCOS Prediction: Random Forest is often effective for PCOS prediction due to its ability to handle complex datasets with multiple features. It can capture non-linear relationships and interactions between features, leading to accurate predictions. However, model performance can vary based on data quality, feature selection, and hyperparameter tuning.

Data Preprocessing

Before training the machine learning models, it’s essential to preprocess the data. This involves steps such as handling missing values, scaling numerical features, encoding categorical variables, and splitting the dataset into training and testing sets.

Model Training and Evaluation

Once the data is preprocessed, we can train different machine learning models using the training set and evaluate their performance using metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve. Hyperparameter tuning techniques such as grid search or random search can be employed to optimize model performance.

To run the PCOS Analyst application locally, follow these simple steps:

1. Install the required dependencies from the `requirements.txt` file using the following command:

pip install -r requirements.txt

2. Load the model from the PKL file into your application. You can do this by importing the necessary libraries and loading the model as follows:

import joblib
# Load the model
loaded_model = joblib.load('your_model.pkl')

3. Run your Streamlit application by executing the following command in your terminal:

streamlit run app.py

4. Once the application is running, open it in your web browser to see it in action.

Link:
- Kaggle Notebook: PCOS Classification Notebook

- Kaggle Datasets: PCOS Data

- GitHub Repository: PCOS Analyst Repo

- Google Drive Notebook: PCOS Google Drive Notebook