Detecting Heart Disease & Diabetes With Machine Learning

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

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Table of Contents

Lecture 3 Whom This Course is Intended for?

Section 2: Tools, IDE, and Datasets

Lecture 4 Tools, IDE, and Datasets

Section 3: Machine Learning Applications in Healthcare

Lecture 5 Machine Learning Applications in Healthcare

Section 4: How Heart Disease & Diabetes Detection Models Work?

Lecture 6 How Heart Disease & Diabetes Detection Models Work?

Section 5: Main Cause of Heart Disease & Diabetes

Lecture 7 Main Cause of Heart Disease & Diabetes

Section 6: Setting Up Google Colab IDE

Lecture 8 Setting Up Google Colab IDE

Section 7: Finding & Downloading Clinical Dataset From Kaggle

Lecture 9 Finding & Downloading Clinical Dataset From Kaggle

Section 8: Uploading Clinical Dataset to Google Colab

Lecture 10 Uploading Clinical Dataset to Google Colab

Section 9: Quick Overview of Clinical Dataset

Lecture 11 Quick Overview of Clinical Dataset

Section 10: Cleaning Dataset by Removing Missing Values & Duplicates

Lecture 12 Cleaning Dataset by Removing Missing Values & Duplicates

Section 11: Finding Correlation Between Blood Pressure & Cholesterol

Lecture 13 Finding Correlation Between Blood Pressure & Cholesterol

Section 12: Analyzing Demographics of Heart Disease Patients

Lecture 14 Analyzing Demographics of Heart Disease Patients

Section 13: Performing Features Importance Analysis with Random Forest

Lecture 15 Performing Features Importance Analysis with Random Forest

Section 14: Building Heart Disease Detection Model with Random Forest

Lecture 16 Building Heart Disease Detection Model with Random Forest

Section 15: Building Heart Disease Detection Model with Logistic Regression

Lecture 17 Building Heart Disease Detection Model with Logistic Regression

Section 16: Finding Correlation Between Blood Glucose & Insulin

Lecture 18 Finding Correlation Between Blood Glucose & Insulin

Section 17: Analyzing Diabetes Cases Caused by Obesity

Lecture 19 Analyzing Diabetes Cases Caused by Obesity

Section 18: Building Diabetes Detection Model with Support Vector Machine

Lecture 20 Building Diabetes Detection Model with Support Vector Machine

Section 19: Building Diabetes Detection Model with XGBoost & KNN

Lecture 21 Building Diabetes Detection Model with XGBoost & KNN

Section 20: Evaluating Accuracy & Performance of Disease Detection Model

Lecture 22 Evaluating Accuracy & Performance of Disease Detection Model

Section 21: Conclusion & Summary

Lecture 23 Conclusion & Summary

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