Unveiling the Impact of Outliers: An Improved Feature Engineering Technique for Heart Disease Prediction

Abstract

Outliers can have a significant impact on statistical analysis and machine learning models, as they can distort the results and adversely affect the performance of the models. Detecting and handling outliers is an important step in data analysis to ensure accurate and reliable results. The techniques, such as Z-scores, box plots, IQR, and local outlier factor analysis, are commonly used in outlier analysis to identify and handle outliers in datasets. These methods can help to detect the data that differ significantly from the remaining data and can be used to determine whether these points should be removed or transformed to improve the accuracy of the models. This research study proposed a Feature Engineering for Outlier Detection and Removal (FEODR) technique by using statistical methods for outlier detection and removal for the heart datasets. This approach aims to enhance the effectiveness of the prediction system by handling outliers effectively. Evaluating the model can provide insights into the effectiveness of the approach in order to improve the effectiveness of the machine learning classifiers.