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Detecting Heart Disease & Diabetes with Machine Learning
Published 5/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 15m | Size: 1.23 GB[/center]
Building heart disease & diabetes detection models using Random Forest, Logistic Regression, SVM, XGBoost, and KNN

What you'll learn
Learn how to build heart disease detection model using Random Forest
Learn how to build heart disease detection model using Logistic Regression
Learn how to build diabetes detection model using Support Vector Machine
Learn how to build diabetes detection model using XGBoost
Learn how to build diabetes detection model using K-Nearest Neighbours
Learn about machine learning applications in healthcare and patient data privacy
Learn how disease detection model works. This section covers data collection, preprocessing, train test split, feature extraction, model training, and detection
Learn how to find correlation between blood pressure and cholesterol
Learn how to analyze demographics of heart disease patients
Learn how to perform feature importance analysis using Random Forest
Learn how to find correlation between blood glucose and insulin
Learn how to analyze diabetes cases that are caused by obesity
Learn how to evaluate the accuracy and performance of the model using precision, recall, and k-fold cross validation metrics
Learn about the main causes of heart disease and diabetes, such as high blood pressure, cholesterol, smoking, excessive sugar consumption, and obesity
Learn how to clean dataset by removing missing values and duplicates
Learn how to find and download clinical dataset from Kaggle

Requirements
No previous experience in machine learning is required
Basic knowledge in Python