MEGHA JOSEPH
October 11, 2022
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✖ dplyr::lag() masks stats::lag()According to statistics,Cardiovascular diseases (CVDs) kill approximately 17 million people globally every year.Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management. Research question is: Which clinical feature will lead to high cardiovascular risk?
1:Behavioral risk factors will not underline significant predictors of predicting Heart Failure.
2:Behavioral risk factors will underline significant predictors of predicting Heart Failure .
I am going to analyze a dataset of 299 patients with heart failure collected in 2015.This dataset is comprised of self-reported survey, with 13 clinical features. data.https://www.kaggle.com/datasets/whenamancodes/heart-failure-clinical-records. The important variables of research are ejection fraction, serum creatinine, and smoking.
.
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-1023-5
---
title: "Project Proposal"
author: "MEGHA JOSEPH"
editor: visual
description: Project proposal part1
date: "10/11/2022"
format:
html:
toc: true
code-fold: true
code-copy: true
code-tools: true
categories:
- finalpart1
---
```{r}
library(tidyverse)
library(dplyr)
```
## Research Question
According to statistics,Cardiovascular diseases (CVDs) kill approximately 17 million people globally every year.Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.
People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management.
Research question is: **Which clinical feature will lead to high cardiovascular risk?**
## Hypothesis
**1:Behavioral risk factors will not underline significant predictors of predicting Heart Failure.**
**2:Behavioral risk factors will underline significant predictors of predicting Heart Failure .**
## Descriptive Statistics
I am going to analyze a dataset of 299 patients with heart failure collected in 2015.This dataset is comprised of self-reported survey, with 13 clinical features. data.https://www.kaggle.com/datasets/whenamancodes/heart-failure-clinical-records.
The important variables of research are ejection fraction, serum creatinine, and smoking.
.
```{r}
library(readr)
mf <- read_csv("C:/Users/user/Downloads/Heart Failure Clinical Records.csv")
summary(mf)
``
```
## References
* Survival prediction of heart failure patients using machine learning techniques *. (n.d.). Retrieved October 11, 2022, from <https://www.sciencedirect.com/science/article/pii/S2352914821002458>
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. <https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-1023-5>