BloodMetrics-ML-Analysis

Advanced Hematological Data Analytics: Machine Learning for Blood Disorder Diagnosis and Research

Overview

This is a groundbreaking project at the intersection of healthcare and technology. Focused on enhancing healthcare decision-making, it leverages advanced data augmentation and machine learning techniques. Utilizing a comprehensive dataset of patient blood count profiles, the project aims to improve patient classification and care strategies.

Features

Data Analysis & Preprocessing: Rigorous data cleaning and normalization, utilizing descriptive statistics, missing value checks, and data visualization.

Classification Model: Powered by XGBoost, the model excels in handling imbalanced datasets with boosted decision trees.

Data Augmentation: Implementation of SMOTE and VAE-based techniques for enriching the dataset and improving model performance.

Model Interpretation: Use of SHAP library for insightful model predictions interpretation.

AI Fairness: Evaluation and mitigation of model biases to ensure fairness.

Results

The project demonstrates significant improvements in model accuracy and robustness, highlighting the potential of sophisticated data handling in revolutionizing healthcare decision-making.

Conclusion

This digital health project underscores the importance of data-driven approaches in healthcare, offering new avenues for personalized patient care and treatment outcomes.