Cardiovascular Disease Diagnosis: A Machine Learning Interpretation Approach

Abstract

Research on heart diseases has always been the center of attention of the world health organization. More than 17.9 million people died from it in 2016, which represent 31% of the overall deaths globally. Machine learning techniques have been used extensively in that area to assist physicians to develop a firm opinion about the conditions of their heart disease patients. Some of the existing machine learning models still suffers from limited predication ability, and the chosen analysis approaches are not suitable. As well, it was noticed that the existing approaches pay more attention to building high accuracy models, while overlooking the ability to interpret and understand the recommendations of these models. In this research, different renowned machine learning techniques: Artificial Neural Networks, Support Vector Machines, Naïve Bayes, Decision Trees and Random Forests have been investigated to help in building, understanding and interpreting different heart disease diagnosing models. The Artificial Neural Networks model showed the best accuracy of 84.25% compared to the other models. In addition, it was found that despite some designed models have higher accuracies than others, it may be safer to choose a lower accuracy model as a final design of this study. This sacrifice was essential to make sure that a more transparent and trusted model is being used in the heart disease diagnosis process. This transparency validation was conducted using a newly suggested metric: the Feature Ranking Cost index. The use of that index showed promising results by making it clear as which machine learning model has a balance between accuracy and transparency. It is expected that following the detailed analyses and the use of this research findings will be useful to the machine learning community as it could be the basis for post-hoc prediction model interpretation of different clinical data sets.

An improved feature selection approach for chronic heart disease detection

“…The features selecting by sequential feature selection algorithm among the 13 heart disease dataset features in the heart disease dataset are the following: The better combination of features selected by the proposed approach are as: Best combination (highest accuracy achieved: 0.971): (0, 1,2,4,6,7,8,9,11,12).…”

“…The researchers studied the effect of high dimensional dataset on the performance of supervised classification model [8], [9]. The authors proposed an information gain based (IFG) feature selection algorithm for reducing a high dimensional input feature for improving classification performance of Naïve Bayes classification algorithm on text data classification.…”

The Role of Machine Learning Algorithms for Diagnosing Diseases

“…Then the model will select the most appropriate class from the majority voting. Figure 6 illustrates the basic idea of the random forest algorithm for diagnosing heart disease [62,7].…”

“…Machine learning models learn from patterns in given training examples without explicit instructions and then use inference to develop useful predictions [7]. Classification methods are widespread in the medical area for identifying and predicting diseases more accurately [8,9,10].…”

Weibull Regression and Machine Learning Survival Models: Methodology, Comparison, and Application to Biomedical Data Related to Cardiac Surgery

“…Several machine learning algorithms were proposed during the last decade for forecasting cardiovascular diseases using different parameters, data sets, and approaches. Diverse machine learning models, such as decision trees, support vector machines, artificial neural networks, naive Bayes, and random forests (RF) were employed to diagnose cardiovascular diseases [ 12 ]. An alternative machine learning technique that has been used to analyze survival data is the random survival forest (RSF) method [ 13 ], which instead of building a single survival tree, creates several of them, each using a random sample of the data.…”