Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc)

Chris Yuan

Last update: Feb 6, 2022

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Machine Learning Heart-Failure-Prediction

Overview

Heart-Failure-Prediction

Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc). Structured a custom ensemble model and a neural network. Found a outperformed model for heart failure prediction accuracy of 88 percent.

Introduction

Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease.

People with cardiovascular disease or who are at high cardiovascular risk need early detection and management wherein a machine learning model can be of great help.

[Data]
- [What We need to do]
[Exploratory Data Analysis]
- [Target Variable]
- [Features]
[Model Selection]
- [Model Creation and Comparison]
- [Bulid a custom ensemble (superlearner) with best three of models]
- [Neural Networks]
- [Feature Importance]
[Conclusion]

DATA

1 Age: Age of the patient [years]

2 Sex: Sex of the patient [M: Male, F: Female]

3 ChestPainType: [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]

4 RestingBP: Resting blood pressure [mm Hg]

5 Cholesterol: Serum cholesterol [mm/dl]

6 FastingBS: Fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]

7 RestingECG: Resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]

8 MaxHR: Maximum heart rate achieved [Numeric value between 60 and 202]

9 ExerciseAngina: Exercise-induced angina [Y: Yes, N: No]

10 Oldpeak: ST [Numeric value measured in depression] (

11 ST_Slope: The slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]

12 HeartDisease: Output class [1: heart disease, 0: Normal]

Reference: https://www.kaggle.com/fedesoriano/heart-failure-prediction

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Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc)

Related tags

Overview

Heart-Failure-Prediction

Introduction

Table of Contents

DATA

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