## 2 Literature Review  
Several previous studies have been conducted in the field of CVD diagnosis. For instance, a new model was proposed for heart disease prediction and referred to as the Heart Disease Prediction Model Using a Hybrid Random Forest (RF) and Linear Model. This exploited several machine learning techniques, including a variety of feature combinations and numerous categorisation algorithms, and achieved an 88.7% accuracy [8]. Generally, research has focused on diagnosing heart disease based on historical data and information. The Smart Heart Disease Prediction model utilised naive Bayesian (NB) techniques to forecast risk factors for heart disease [9]. The results revealed that this diagnostic approach was highly successful at predicting risk factors for CVDs with an accuracy of 89.77% [9]. [PERSON] et al. [10] published an efficient heart disease prediction model for a clinical decision support system that utilised density-based spatial clustering of applications with noise (DBSCAN) in order to identify and eliminate outliers, in conjunction with a hybrid synthetic minority over-sampling technique-edited nearest neighbour technique. Two publicly available data sets, Statlog and Cleveland, were used to evaluate the model and to compare its performance against other widely used classifier models. The proposed model outperformed previous models, achieving an accuracy of up to 96%. Various artificial intelligence strategies for coronary artery heart disease prediction were compared using seven computational intelligence approaches, namely logistic regression (LR), support vector machine (SVM), a deep neural network (DNN), decision tree (DT), NB techniques, RF, and k-nearest neighbour (K-NN) [11]. The Statlog and Cleveland heart disease data sets were used to assess each technique's performance. DNNs were found to achieve the highest accuracy, i.e., 98.15%. Compared to the state-of-the-art methods, and focusing on heart disease prediction, their approach outperformed prior studies [11]. A novel method for predicting heart disease was also presented that included all techniques in a single algorithm, i.e., the hybridisation technique [12]. Several methods were listed and evaluated in order to determine their accuracy level, and the results showed that a correct diagnosis was possible when using a composite model comprising all approaches. An accuracy of 89.2% was attained. A further paper utilised an optimum feature extraction method to generate a de novo clustering model for the prediction of CVD using numerical data and electrocardiographic (ECG) data [13]. Rather than directly grouping the numerical data, a principal component analysis was used for dimensionality reduction. The hybrid clustering technique consisted of improved DBSCAN combined with optimised k-means clustering (KMC). Simultaneously, four data sets were utilised, i.e., two data sets and an ECG data set, which is a test used to detect and to record the timing and intensity of human cardiac electrical activity. The findings indicated that the model effectively resolved the issues associated with using dual data for heart disease prediction. A heart disease prediction system was also developed using the data mining approach by combining NB techniques and KMC [14]. This approach facilitated the prediction of CVD by incorporating numerous variables and providing output data in the prediction form, using the k-means and NB algorithms to group a range of factors and to perform prediction, respectively. Unsupervised KMC has also been utilised in the clinical domain to detect anomalies as a method of CVD prediction [15]. The suggested model produced an ideal value for k by forming clusters to recognise anomalies using the silhouette approach. Identified anomalies were excluded from the data, and the resulting prediction model was created using the five most prominent machine learning classification approaches, namely KNN, RF, SVM, the NB technique and LR.