Diagnosing Coronary Heart Disease using Ensemble Machine Learning

Abstract

Abstract-Globally, heart disease is the leading cause of death for both men and women. One in every four people is afflicted with and dies of heart disease. Early and accurate diagnoses of heart disease thus are crucial in improving the chances of longterm survival for patients and saving millions of lives. In this research, an advanced ensemble machine learning technology, utilizing an adaptive Boosting algorithm, is developed for accurate coronary heart disease diagnosis and outcome predictions. The developed ensemble learning classification and prediction models were applied to 4 different data sets for coronary heart disease diagnosis, including patients diagnosed with heart disease from Cleveland Clinic Foundation (CCF), Hungarian Institute of Cardiology (HIC), Long Beach Medical Center (LBMC), and Switzerland University Hospital (SUH).The testing results showed that the developed ensemble learning classification and prediction models achieved model accuracies of 80.14% for CCF, 89.12% for HIC, 77.78% for LBMC, and 96.72% for SUH, exceeding the accuracies of previously published research. Therefore, coronary heart disease diagnoses derived from the developed ensemble learning classification and prediction models are reliable and clinically useful, and can aid patients globally, especially those from developing countries and areas where there are few heart disease diagnostic specialists.

Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

“…These data were all collected from the UCI machine learning repository. Further studies have used the Cleveland dataset only since not lacking values [11], [13], [16]- [19]. In contrast, other datasets showed more than 90% of some attributes' missing values which might compromise the accuracy and the quality of results, e.g "thal"and "ca" attributes that shown to have high correlation with the output attribute.…”

“…The number of selected attributes and standard features was ranging from 76 to 8, including the class attribute. Generally, the studies that used many attributes have applied feature selection to improve the relevance [19], [22]. Hence, we perform only 14 attributes, including (Age, Gender, Chest pain, blood pressure, etc.)…”

“…Also, it is a costly and time-consuming Author [18] suggested a predictive model using C4.5 and fast decision tree algorithms applied on the four collected and separated UCI datasets; this model achieved an accuracy of 78.06% and 75.48% for C4.5 and fast decision tree, respectively, using only the Cleveland dataset. Author [19] predicted HD using the meta-algorithm AdaBoost on the Cleveland dataset and suggested reducing the number of attributes from 76 to 28 to provide higher accuracy of 80.14%. A comparative study using four different classifiers, including SVM, KNN, C5.0, and Neural network, was approved by Ref.…”

The Role of Data Pre-processing Techniques in Improving Machine Learning Accuracy for Predicting Coronary Heart Disease

“…Also, the researchers in [4] contributed to the literature of this field by using boosting adaptive algorithm on four datasets, namely (UCI Cleveland, UCI Switzerland, UCI Long Beach, and UCI Hungarian) to diagnose coronary heart disease. This approach obtained accuracy (97.16% and 80.14% for Cleveland, 98.63% and 89.12% for Hungarian, 93.15% and 77.78% for Long Beach, 100% and 96.72% for Switzerland) for training and testing set respectively.…”

“…Heart disease is a term that refers to any problem that can affect the heart and blood vessels [2], such as coronary heart disease, congenital heart disease, and rheumatic heart disease [4], which, according to the National Heart, Lung, and Blood Institute ranks among the most dangerous and common diseases in the world.…”