# 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.