Predictive Systems: Role of Feature Selection in Prediction of Heart Disease

Abstract

As per recent trends heart disease has become the major factor for untimely deaths. There are huge amounts of clinical data available from biomedical devices and various applications used by hospitals. Artificial Intelligence is rigorously being used in predicting conditions of heart patients. This is mainly achieved by machine learning where a model is trained with sample cases and is then used for prediction of the ailment as per data available from clinical tests of the patient. This paper focuses in analyzing the accuracy of various classification algorithms, when they are supervised by set of features. Feature selection plays an important role in eliminating redundant and irrelevant features and reduces the training cost and time of the predictive models. The classification algorithms, which have been analyzed include Naive Bayes, Random Forest, Extra Trees and Logistic regression which have been provided with selected features using least absolute shrinkage and selection operator (LASSO) and Ridge regression. The accuracy of the classifiers shows remarkable improvement after using feature selection. The prediction has improved on an average by 33.3% using Lasso regression as compared to 30.73% using ridge regression.

Predicting the Causal Effect Relationship Between COPD and Cardio Vascular Diseases

“…Results of the six Classification Algorithms have been noted in Table 2 with their confusion matrix/accuracy in predicting the test samples. [16] has been done using the heat map attached in Figure 4 and the factors which are mostly responsible for causing COPD have been determined as Cor Pulmonale, Age, and Smoking. These factors are then studied with several algorithms to analyze their performance in terms of accuracy, precision, and recall.…”

Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

“…Panda et.al [9] has discussed about the Lasso and ridge Regression in Heart disease prediction. Accuracy have been noted with Lasso and Ridge separately.…”

“…The penalty is added here by using both L1-norm and L2-norm. It can be used as Ridge and Lasso by setting the parameter to 1 or 0 [9].…”

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