Detecting Heart Disease & Diabetes with Machine Learning

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Free Download 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
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
Description
Welcome to Detecting Heart Disease & Diabetes with Machine Learning course. This is a comprehensive project based course where you will learn step by step on how to build heart disease and diabetes detection models using Random Forest, XGBoost, logistic regression, and support vector machines. This course is a perfect combination between machine learning and healthcare analytics, making it an ideal opportunity for you to level up your data science and programming skills. In the introduction session, you will learn about machine learning applications in the healthcare field, such as getting to know its use cases, models that will be used, patient data privacy, technical challenges and limitations. Then, in the next section, we are going to learn how heart disease and diabetes detection models work. This section will cover data collection, data preprocessing, splitting the data into training and testing sets, model selection, mode training, and disease detection. Afterward, you will also learn about the main causes of heart disease and diabetes, for example, high blood pressure, high cholesterol, obesity, excessive sugar consumption, and genetics. After you have learnt all necessary knowledge about the disease detection model, we will start the project. Firstly you will be guided step by step on how to set up Google Colab IDE. In addition to that, you will also learn how to find and download clinical dataset from Kaggle. Once everything is ready, we will enter the first project section where you will explore the clinical dataset from multiple angles, not only that, you will also visualize the data and make sure you understand the data pattern. In the second part, you will learn step by step on how to build heart disease and diabetes detection systems using Random Forest, XGBoost, logistic regression, and support vector machines. Meanwhile, in the third part, you will learn to evaluate the model's accuracy and performance using several methods like k-fold cross validation, precision, and recall methods. Lastly, at the end of the course, we will conduct testing on the disease detection model to make sure it has been fully functioning and the detected result is accurate.First of all, before getting into the course, we need to ask ourselves this question, why should we build heart disease and diabetes detection models? Well, here is my answer. Machine learning presents an extraordinary opportunity to elevate healthcare standards by enabling early disease detection. By developing precise models for identifying heart disease and diabetes, we can initiate timely interventions, personalise treatment plans, and proactively manage health concerns. This not only enhances patient outcomes but also streamlines healthcare delivery systems, reducing the burden on healthcare providers and curbing healthcare expenses over time. In essence, these models signify a significant leap in leveraging technology to boost healthcare accessibility, efficiency, and affordability. Last but not least, by building these projects, you will gain valuable skills and knowledge that can empower you to make a difference in the world of healthcare and potentially open lots of doors to endless opportunities.Below are things that you can expect to learn from this course:Learn about machine learning applications in healthcare and patient data privacyLearn how heart disease and diabetes detection models work. This section will cover data collection, preprocessing, train test split, feature extraction, model training, and detectionLearn about the main causes of heart disease and diabetes, such as high blood pressure, cholesterol, smoking, excessive sugar consumption, and obesityLearn how to find and download clinical dataset from KaggleLearn how to clean dataset by removing missing values and duplicatesLearn how to find correlation between blood pressure and cholesterolLearn how to analyse demographics of heart disease patientsLearn how to perform feature importance analysis using Random ForestLearn how to build heart disease detection model using Random ForestLearn how to build heart disease detection model using Logistic RegressionLearn how to find correlation between blood glucose and insulinLearn how to analyse diabetes cases that are caused by obesityLearn how to build diabetes detection model using Support Vector MachineLearn how to build diabetes detection model using XGBoostLearn how to build diabetes detection model using K-Nearest Neighbors Learn how to evaluate the accuracy and performance of the model using precision, recall, and k-fold cross validation metrics
Who this course is for
People who are interested in building heart disease and diabetes detection models using Random Forest, Logistic Regression, SVM, XGBoost, and KNN
People who are interested in machine learning application in healthcare field
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