Page 52 - AIH-1-1

Artificial Intelligence in Health AI model for cardiovascular disease prediction

f
ρ = δ s k (I)
k
∑ k =1 f k
Where ρ is the probability of an individual selection, f
k
K
is an individual fitness, and δs is the population size.
3.2. Machine learning algorithm for CVD prediction
model
Similarly, some of the popular SMLAs, such as K-means,
KNN, SVM, and DT, were further utilized for the training
and prediction of CVDs to carry out a comparative
analysis of the prediction model. The CVD dataset was
obtained from the UCI repository, which contains about
76 cardiac attributes for the training in various machine
learning models mentioned. This CVD dataset consists of
14 attributes, including age, sex, chest pain type, resting
blood pressure, serum cholesterol (mg/dl), fasting blood
sugar, resting electrocardiographic result, maximum heart
Figure 1. Overview of genetic algorithm for feature selection. rate, exercise-induced angina, oldpeak, slope of the peak,

Figure 2. The flowchart for artificial neural network-genetic algorithm training model.

Volume 1 Issue 1 (2024) 46 https://doi.org/10.36922/aih.1746