# Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
###### Abstract
Researchers have designed and developed several clinical decision support systems [9], and automated diagnostic systems based on machine learning and data mining for various disease conditions, such as hepatitis [10], dementia [11; 12], and heart disease [13; 14]. One of these methods was presented by [PERSON] et. al., who proposed a deep neural network-based diagnostic system for cardiovascular disease prediction. The authors claim that the \(\chi^{2}\) model for feature classification in their proposed work has an accuracy of 93% [14]. [PERSON] et al. proposed an intelligent learning system based on a random search algorithm and RF for early heart disease prediction. Other researchers also developed other methods. The accuracy of their proposed model was 93.33%, which was the highest possible [15]. [PERSON] et al. [16] used ECG data to predict cardiac issues early. The method uses a two-stage predictive framework for processing ECG data. A global classification factor compared abnormalities with a universal reference model. [PERSON] et al. [17] built a health monitoring system to identify patients with heart failure. A data mining strategy using the CART method and HRV was used for feature extraction. The proposed method achieved 96.39% accuracy in identifying heart failure cases. The accuracy in determining the severity of heart failure was 79.31%. The publicly available RR Interval database was used for the studies as the data source for heart failure. The dataset contained information from 83 participants, 54 of whom were healthy and 29 of whom were diagnosed with heart failure. [PERSON] et al. [18] utilized a fuzzy resolution process for heart failure detection. The proposed solution involves a combination of ANN and fuzzy logic. The approach was evaluated using an open-source dataset of cardiac disease from Cleveland. The accuracy of the proposed ANFIS model was 91.83%. MATLAB was used for all experiments. [PERSON] et al. [19] used an enhanced random survival forest to make a model that predicts how long heart failure patients will live in the hospital. A public dataset from the MIMIC II clinical database was used for the trials. This dataset had 8059 patients and 32 characteristics. The suggested approach had 82.01% accuracy. [PERSON] [20] developed several machine learning techniques to predict the survival of cardiac patients. In his proposed work, the decision tree achieved the highest accuracy (85%) compared to the other supervised machine learning techniques. [PERSON] and [PERSON] [21] developed six models; each developed model used eight machine learning classifiers, such as logistic regression (LR), decision tree (DT), support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), K-nearest neighbor (KNN), and Naive Bayes (NB). Other measures, such as precision, recovery rate, F1 score, support score, and AUC/ROC were calculated to support the accuracy assessment of the model. The highest accuracy improvement was achieved by linear discriminant analysis with a performance of 80.61%,as well as 83.17% by RF in one model, 83.12% by LDA, and 83.05% by LR. Table 1 provides a summary of previous studies on the prediction of heart failure and mortality in cardiac patients using machine learning methods  
However, the prediction of heart failure refers to identifying patients at risk of developing heart failure in the future. This may include assessing risk factors such as age, hypertension, diabetes, obesity, and history of cardiovascular disease. Early identification of patients at risk of heart failure allows timely interventions to prevent the development of heart failure and improve outcomes. On the other hand, predicting mortality in heart patients involves identifying patients at risk of dying from their heart disease. Factors such as the severity of the disease, the patient's age, general health, and the presence of comorbidities can be taken into account.