Employing feature engineering strategies to improve the performance of machine learning algorithms on echocardiogram dataset

OBJECTIVES: This study mainly uses machine learning (ML) to make predictions by inputting features during training and inference. The method of feature selection is an important factor affecting the accuracy of ML models, and the process includes data extraction, which is the collection of all data required for ML. It also needs to import the concept of feature engineering, namely, this study needs to label the raw data of the cardiac ultrasound dataset with one or more meaningful and informative labels so that the ML model can learn from it and predict more accurate target values. Therefore, this study will enhance the strategies of feature selection methods from the raw dataset, as well as the issue of data scrubbing. METHODS: In this study, the ultrasound dataset was cleaned and critical features were selected through data standardization, normalization, and missing features imputation in the field of feature engineering. The aim of data scrubbing was to retain and select critical features of the echocardiogram dataset while making the prediction of the ML algorithm more accurate. RESULTS: This paper mainly utilizes commonly used methods in feature engineering and finally selects four important feature values. With the ML algorithms available on the Azure platform, namely, Random Forest and CatBoost, a Voting Ensemble method is used as the training algorithm, and this study also uses visual tools to gain a clearer understanding of the raw data and to improve the accuracy of the predictive model. CONCLUSION: This paper emphasizes feature engineering, specifically on the cleaning and analysis of missing values in the raw dataset of echocardiography and the identification of important critical features in the raw dataset. The Azure platform is used to predict patients with a history of heart disease (individuals who have been under surveillance in the past three years and those who haven’t). Through data scrubbing and preprocessing methods in feature engineering, the model can more accurately predict the future occurrence of heart disease in patients.