GlucoPredict: Diabetes Risk Detection with Machine Learning
Project Overview
This project focuses on building a machine learning model to classify individuals as diabetic or non-diabetic based on health indicators like glucose level, BMI, and age. It uses labeled medical data and applies classification algorithms to assist in early diagnosis.
Objective
To create a supervised learning model that predicts the risk of diabetes using medical data, supporting preventive healthcare strategies.
Key Skills Applied
- Medical Data Analysis
- Feature Engineering (BMI, Age Groups, Glucose Levels)
- Supervised Learning (Logistic Regression, KNN, Random Forest)
- Model Evaluation (Accuracy, Recall, ROC-AUC)
Tools & Libraries Used
Python, Pandas, NumPy, Scikit-learn, Matplotlib
Learning Outcomes
- Understood how to work with structured health datasets (e.g., PIMA Indian Diabetes dataset)
- Learned to detect patterns in health indicators
- Practiced tuning models for sensitive healthcare predictions
- Emphasized importance of recall and false negatives in medical ML
Result
Built a model with [e.g., 85% accuracy and 90% recall], capable of flagging high-risk individuals for early medical intervention.