Prediction of Heart Failure Condition and SurvIVAl Methods Using Machine Learning

1. PREDICTION OF HEART FAILURE CONDITION AND SURVIVAL
METHODS USING MACHINE LEARNING

2. INTRODUCTION
 The number of people with heart failure has increased rapidly worldwide.
 Several different symptoms are associated with heart disease, which makes it
difficult to diagnose it quicker and better.
 This issue can be resolved by adopting machine learning techniques.
 There are conflicting views and an overall lack of research as to how best to
manage these heart failure conditions and their survival methods.

3. OBJECTIVES
Several researchers use machine learning techniques for predicting HF conditions.
Yet, these researchers compared a set of machine learning algorithms with some
simple primitive techniques. This inspired me to think about the complexity of
machine learning algorithms for predicting heart failure and to assess the methods
using a common benchmark dataset.
RESEARCH QUESTIONS
 Which type of machine learning algorithms are used for predicting heart disease?
 Most effective machine learning approach to evaluate heart failure conditions and
their survival methods?
 Which variable can cause heart failure condition more?

4. PROGRAMMING RESOURCES & MLAlgorithms
 Open-source IDE called RStudio. The programs are written in R script. Windows 10
system is used for these purposes.
 Standard UCI Cleveland cardiac disease dataset is retrieved from the Kaggle
database.
 Machine learning algorithms used for testing the results are,
1. Linear regression
2. Logistic regression
3. Decision tree
4. Random forest
5. Artificial Neural Network
6. K-Nearest Neighbor
7. Support Vector Machine
8. Naive Bayes
9. Gradient Boosting

5. METHODOLOGY
Splitting
Dataset
Data
Collection
Dataset creation
Loading
manipulation
Data
cleaning &
pre-
processing
1. Missing values
2. Outlier detection
Data
Visualizati
on
Boxplot
Bar chart
Q-Q plot
Histogram
Data
Exploration
Correlation analysis
Partial correlation
Hypothesis
testing(KS test)
Factor analysis
Model
Creation &
Evaluation
Survival
Methods
GUI
Modelling

6. DATA VISUALIZATION

7. DATA EXPLORATION
 Both the spearman correlation method and the pearson
correlation approach can be used to determine the
connection.
 Variables chosen after factor reduction are age, bp,
FBS.Over.120, cholesterol, sex, exercise.Angina,
ST.Depression, and slope.Of.St
 After analysing the partial correlation across the terms,
we could find that the terms age, BP, FBS.Over.120,
cholesterol, sex, exercise.Angina, ST.Depression, and
slope.Of.ST shows a positive estimate value, which
means a strong correlation with total heart disease. So,
for further analysis, I could select the above variables.


8. MODEL CREATION AND EVALUATION

10. SURVIVAL METHODS
NON- PARAMETRIC SURVIVAL
METHOD
• KAPLAN-MEIER SURVIVAL METHOD
PARAMETRIC SURVIVAL METHOD
• EXPONENTIAL MODEL
• LOG-NORMAL MODEL
• COX PROPORTIONAL HAZARD
MODEL

11. GRAPHICAL USER INTERFACE (GUI)

12. CONCLUSION
1. Which type of machine learning algorithms are used for predicting heart disease?
Decision tree, logistic regression, linear regression, k-Nearest Neighbor, random forest, Gradient
boosting, Artificial neural network, Naïve Bayes, and Support vector machine.
2. Most effective machine learning approach to evaluate heart failure conditions and their survival
methods?
Linear Regression and Naive Bayes seem to be the best models, with the highest accuracy of
0.8518519. Here I used a non-parametric method like the Kaplan-Meier survival curve and
parametric survival methods like the Exponential model, Log – Normal model and Cox proportional
hazard model (which is a semi-parametric model).
3. Which variable can cause heart failure condition more?
variables Age, Blood Pressure, Maximum Heart Rate, Thallium, Number of vessels Fluro, and Age
can cause heart failure condition more.

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14. THANKYOU

15. Any
Questions?