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    Detecting Heart Disease & Diabetes With Machine Learning

    Posted By: ELK1nG
    Detecting Heart Disease & Diabetes With Machine Learning

    Detecting Heart Disease & Diabetes With Machine Learning
    Published 5/2024
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 1.42 GB | Duration: 3h 15m

    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