Machine Learning - Prediction of CHD Risk with R

Data science project in R to create a predictive model of the CHD risk, based on supervised learning.

Paper Analysis Code


In the current era in which we live, there is a clear and irreversible tendency to generate and store large volumes of information, from various sources such as: government agencies, public and private companies, clinics and hospitals, social networks, etc. Hence the great need to analyze the data in order to obtain some benefit for their owner, a third party or humanity in general. With this in mind, we conducted a descriptive and predictive analysis of public medical data of South Africa on patients with possible risk of presenting coronary heart disease (CHD), and applying advanced techniques of supervised machine learning and models calibration, we were able to determine when a person has high probabilities (close to 70%) of presenting or developing this disease, with the objective of being able to contribute to an early detection and diagnosis of it, for further treatment. Hopeful and convincing results were obtained, which can be improved if there is a greater amount of source data from which to learn.


The dataset used is a sample of 462 records of a larger dataset, described in Rousseauw et al, 1983, South African Medical Journal, belonging to a non-profit organization called South African Heart Association (SAHA).

# Variable Type Description Data Type
1 sbp Input Systolic Blood Pressure Numerical
2 tobacco Input Cumulative Tobacco (kg) Numerical
3 ldl Input Low Density Lipoprotein Cholesterol Numerical
4 adiposity Input Adiposity Numerical
5 famhist Input Family history of heart disease (Present, Absent) Categorical
6 typea Input Type-A behavior Numerical
7 obesity Input Obesity Numerical
8 alcohol Input Current alcohol consumption Numerical
9 age Input Age at onset Numerical
10 chd Target Coronary heart disease (Yes, No) Categorical


Based on the following results, the Naive Bayes model was selected as the best predictor for the problem we are solving, because it has an excellent Global Average Error of 28.77% and is the one with the highest YES detection index (key indicator), with 61.88%

Model % Global Error % Yes Detected % No Detected
SVM 28.71 50.00 82.45
Naive Bayes 28.77 61.88 76.16
Decision Tree 31.22 48.75 79.47
Random Forest 31.77 41.88 82.12
K-NN 33.08 41.88 80.46
Base Line 34.63 NA NA
Ada Boost 34.95 43.13 76.82

Technologies and Techniques

R Dependencies


If you need to install a package, use the following command in the R console:

  install.packages("package-name", dependencies=TRUE)

Contributing and Feedback

Any kind of feedback/criticism would be greatly appreciated (algorithm design, documentation, improvement ideas, spelling mistakes, etc…).



This project is licensed under the terms of the MIT license.