PCOS Prediction Using Classification Algorithms

Machine learning models for the early detection of Polycystic Ovary Syndrome (PCOS) from routine clinical, hormonal, and lifestyle data — comparing four classifiers to find the most reliable predictor for at-risk screening.

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Overview

PCOS is one of the most common endocrine disorders in women of reproductive age, affecting an estimated 8–13% of women — yet up to 70% of cases go undiagnosed. Left unaddressed, it raises the risk of insulin resistance, obesity, infertility, and cardiovascular complications.

This project builds and compares four supervised classification models that predict a PCOS diagnosis from a patient's demographic, clinical, hormonal, and lifestyle attributes, with the goal of supporting earlier, data-driven screening.

Headline result: a Random Forest classifier achieved 91% accuracy and a 0.86 F1-score on the PCOS-positive class — the strongest of the four models evaluated.

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Dataset

• Source: PCOS Dataset — Kaggle
• File: PCOS_data_without_infertility.xlsx (sheet 2)
• Size: 541 patients × 45 columns (reduced to 42 after cleaning)
• Target: PCOS (Y/N) — binary (0 = No, 1 = Yes)
• Class balance: 364 negative / 177 positive (32.7% positive)

Feature groups (41 predictors):

Category	Example Features
Demographics	Age, Weight, Height, BMI, Blood Group
Vitals	Pulse rate, Respiration rate, BP (Systolic/Diastolic)
Blood work	Hb, FSH, LH, FSH/LH, TSH, AMH, PRL, Vitamin D3, PRG, RBS, β-HCG
Clinical indicators	Follicle count (L/R) & size, Endometrium thickness, Cycle length/regularity
Lifestyle	Fast-food consumption, Regular exercise
Symptoms	Weight gain, Hair growth, Skin darkening, Hair loss, Pimples

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Methodology

1. Data cleaning — trimmed column-name whitespace; dropped non-informative columns (Sl. No, Patient File No., Unnamed: 44).
2. Type correction — coerced object-typed AMH(ng/mL) and II beta-HCG(mIU/mL) to numeric.
3. Missing values — imputed 4 columns (Marriage Status, II beta-HCG, AMH, Fast food) with their median.
4. Exploratory analysis — correlation heatmap, box plots (numerical), and bar plots (categorical) to identify the strongest signals.
5. Modeling — trained four classifiers on a 70/30 train-test split (random_state=0):
   • Linear models: Logistic Regression, Support Vector Machine (SVM)
   • Non-linear models: Gaussian Naive Bayes, Random Forest
6. Evaluation — compared models on accuracy, precision, recall, F1-score, specificity, and Matthews Correlation Coefficient (MCC), prioritizing recall to minimize missed positive cases in a screening context.

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Key Findings from EDA

Features most correlated with a PCOS diagnosis:

Feature	Correlation
Follicle No. (Right)	0.65
Follicle No. (Left)	0.60
Skin darkening	0.48
Hair growth	0.46
Weight gain	0.44
Cycle (Regular/Irregular)	0.40
Fast food consumption	0.38

Interpretation: a higher follicle count and the classic hyperandrogenic symptoms (skin darkening, excess hair growth, weight gain) are the dominant predictors — consistent with clinical understanding of PCOS.

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Results

Evaluated on the held-out test set (163 patients). Metrics are for the PCOS-positive class.

Model	Accuracy	Precision	Recall	F1-Score
Random Forest	0.91	0.93	0.80	0.86
Logistic Regression	0.84	0.82	0.67	0.73
Naive Bayes	0.83	0.81	0.63	0.71
SVM	0.67	0.00	0.00	0.00

Extended metrics:

Model	Specificity	False Negative Rate	MCC
Random Forest	0.97	0.20	0.80
Logistic Regression	0.93	0.33	0.63
Naive Bayes	0.93	0.37	0.60
SVM	1.00	1.00	0.00

Analysis

• Random Forest is the clear winner — highest accuracy (0.91), F1 (0.86), and MCC (0.80), with the lowest false-negative rate among usable models. Its ensemble of trees captures the non-linear interactions between follicle counts, hormones, and symptoms.
• Logistic Regression and Naive Bayes are solid, interpretable baselines (~0.83–0.84 accuracy) but miss roughly a third of positive cases (recall 0.63–0.67).
• SVM failed to learn — it predicted the majority class for every sample (0.67 accuracy but 0.00 recall). This is a textbook consequence of training an unscaled SVM on features spanning wildly different ranges (e.g., hormone levels vs. binary flags). It's included here as an honest baseline and a reminder that feature scaling is essential for margin-based models.

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Tech Stack

• Language: Python (Jupyter Notebook)
• Libraries: pandas, NumPy, scikit-learn, Matplotlib, Seaborn, openpyxl

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Getting Started

# 1. Clone the repository
git clone https://github.com/Ashi777/PCOS-Prediction-Using-Classification-Algorithms.git
cd PCOS-Prediction-Using-Classification-Algorithms

# 2. Install dependencies
pip install pandas numpy scikit-learn matplotlib seaborn jupyter openpyxl

# 3. Launch the notebook
jupyter notebook PCOS-Prediction-Using-Classification-Algorithms.ipynb

Download PCOS_data_without_infertility.xlsx from the Kaggle dataset and update the file path in the data-loading cell.

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Future Work

• Feature scaling (StandardScaler) so SVM and Logistic Regression can compete fairly
• Hyperparameter tuning (GridSearchCV) and k-fold cross-validation for robust estimates
• Class-imbalance handling (SMOTE / class weights) to further improve positive-class recall
• ROC-AUC comparison and feature-importance ranking from the Random Forest
• Deploying the best model behind a simple prediction API

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Disclaimer

This project is for educational and research purposes only and is not a substitute for professional medical diagnosis.

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License

Released under the MIT License.

Author

Ashi Malaiya — GitHub · LinkedIn