# Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
###### Abstract  
Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clinical decision support system for predicting mortality in cardiac patients to address this problem. The dataset collected for the experimental purposes of the proposed model consisted of 55 features with a total of 368 samples. We found that the classes in the dataset were highly imbalanced. To avoid the problem of bias in the machine learning model, we used the synthetic minority oversampling technique (SMOTE). After balancing the classes in the dataset, the newly proposed system employed a \(\chi^{2}\) statistical model to rank the features from the dataset. The highest-ranked features were fed into an optimized random forest (RF) model for classification. The hyperparameters of the RF classifier were optimized using a grid search algorithm. The performance of the newly proposed model (\(\chi^{2}\)-RF) was validated using several evaluation measures, including accuracy, sensitivity, specificity, F1 score, and a receiver operating characteristic (ROC) curve. With only 10 features from the dataset, the proposed model \(\chi^{2}\)-RF achieved the highest accuracy of 94.59%. The proposed model \(\chi^{2}\)-RF improved the performance of the standard RF model by 5.5%. Moreover, the proposed model \(\chi^{2}\)-RF was compared with other state-of-the-art machine learning models. The experimental results show that the newly proposed decision support system outperforms the other machine learning systems using the same feature selection module (\(\chi^{2}\)). a 2023 13 5188 1 1  
Agn Research Center, Karolinska Institutet, 171 65 Stockholm, Sweden  
Department of Health, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden  
Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Chulalongkorn University, Bangkok 103 30, Thailand  
Department of Electrical Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan  
School of Health Sciences, University of Skovde, 541 28 Skovde, Sweden
the early detection of the disease to save countless lives. Traditional medical treatments include diagnostic tests to detect heart diseases, such as ausculation, blood pressure, pulse rate, electrocardiogram (ECG), and blood glucose levels. These tests are expensive and time-consuming; the patient may be unable to wait and may need immediate treatment [5]. There are many types of heart disease, such as coronary, cardiovascular, and cardiomyopathy. Cardiovascular diseases affect blood flow and veins and are usually related to the heart [6]. The diagnosis of coronary disease can be divided into three main tasks. First, datasets are analyzed, then multiple designs are created, and finally, the design that combines the lowest possible cost with the greatest possible efficacy is selected. Finally, the correct information is extracted from the datasets, and the resulting observations are made. Since there are many risk factors, such as high blood pressure, a fast heartbeat, high cholesterol, diabetes, the heart, and other parameters, it is difficult to predict the mortality rate related to the heart. Certain data mining techniques, such as neural networks and machine learning, are used to determine the severity of the disease. Other methods, such as Naive Bayes (NB), decision tree (DT), K-nearest neighbor (kNN), and genetic algorithm (GA) are also used in classifying the severity of disease [7]. Heart diseases are difficult to treat because they have complex effects on patients' lives; therefore, they are difficult to manage, and if not treated carefully, they can cause severe damage to the heart, ultimately leading to the patient's death [8].