Stacking ensemble based hyperparameters to diagnosing of heart disease: Future works

Abstract

Background: Heart disease is one of the most recurrent and worrying health problems today, due to its multiple complications, including: stroke, cardiac arrest, retinopathy, etc. Objective: Propose a method and 4 Stacking models based on hyperparameters to diagnose heart disease. In addition, a web interface was developed with the best model proposed in this study. Methods: First, the dataset used was from the Heart Disease Cleveland ICU, which was 918 patient records and 12 attributes. Therefore, the paper was composed of the following stages: Cleaning and Pre-processing; Describe data; Training and testing data; Cross validation; Calibration of models; and modelling and evaluation, also compare the different techniques proposed to predict heart disease using Stacking ensemble based on hyperparameters taking into account the performance evaluation parameters. Results: Stacking 1 (Logistic regression) with oversampling and AdaBoost-SVM with hyperparameter in the test obtained higher Accuracy (88.24%), and ROC Curve (92.00%), while too Stacking 1 (Logistic regression) with oversampling reached a better Precision (88.54%), but the AdaBoost-SVM algorithm using hyperparameter achieved a high value of Sensitivity (88.14%) and F1-Score (88.19%). Conclusions: Implementing 4 stacking models based on hyperparameters, it helps to make an early diagnosis of heart disease and greater precision, and decrease the quantity of deceases caused by it. Therefore, by using the combined method, an improvement in heart disease prediction was observed, surpassing the performance of the independent algorithms used.

Keywords

• Machine learning
• Prediction
• Heart disease
• Hyperparameters
• Stacking