Detecting Heart Disease & Diabetes with Machine Learning

Building heart disease & diabetes detection models using Random Forest, Logistic Regression, SVM, XGBoost, and KNN

[Free Udemy Course] Detecting Heart Disease & Diabetes with Machine Learning

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

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