Machine Learning Assignment
Heart Failure Prediction
Description
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 worlwide. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure. 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 wherein a machine learning model can be of great help.
Now, we will be using KNN algorithm with hyperparametertuning for prediciting the heart failure because it is simple to implement.
Here are the couple of steps we can follow inorder to implement the KNN algorithm with hyperparametertuning.
- Determine the value of K.
- Calculate the distance of new data with training data.
- Find the closest K-neighbors from the new data.
- New Data Class Prediction.
- Evaluation and calculate the accuracy of the model, if the accuracy is still low, then this process can be repeated again from beginning.