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A supervised machine learning project that predicts analgesic treatment effectiveness based on patient characteristics and pain type. Compares five classification algorithms with hyperparameter optimization to identify the most effective pain management strategy for individual patient profiles.
ai
Internal / Client
4 tools
Standard
Chronic pain patients frequently cycle through multiple medications before finding one that works. A data-driven approach could reduce this trial-and-error period and improve quality of life.
Pain management often relies on trial-and-error, with no clear way to predict which analgesic will be effective for a given patient profile. The dataset was also imbalanced, requiring careful stratification and class-weight adjustments.
Machine learning researcher — collected and preprocessed the medical dataset, engineered features from patient profiles, implemented and optimized five classification models, performed cross-validation and hyperparameter tuning, and visualized comparative performance results.
Built a comparative ML pipeline testing five algorithms (Logistic Regression, Random Forest, SVM, KNN, MLP) with GridSearch optimization and class imbalance handling, using Scikit-learn and Python data science tools.
The dataset was imbalanced — certain analgesics were overrepresented. Required careful stratification and class-weight adjustments. Medical data also needed extensive cleaning and normalization.
A comprehensive performance comparison of five ML models for analgesic effectiveness prediction, with optimized versions showing improved accuracy through hyperparameter tuning and cross-validation.
Deepened understanding of class imbalance handling, feature importance analysis, and the practical challenges of applying ML to real-world healthcare data.
This project tackles the clinical challenge of predicting which analgesic treatment will be most effective for a given patient. Using a medical dataset of patient profiles and treatment outcomes, the notebook performs comprehensive exploratory data analysis, handles missing values, and trains five classification models: Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) Neural Network. Each model is first trained in its default configuration, then optimized via GridSearch cross-validation. The final section compares all model performances using accuracy metrics and visualizations to identify the best-performing approach for personalized analgesic recommendation.
A data analysis and machine learning project exploring the World Happiness Report dataset. Uses linear regression, polynomial regression, and K-Means clustering to identify factors influencing global happiness scores and group countries by well-being patterns.
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