## 2 Literature Review
### Review on Heart Disease Prediction by Using Soft Computing Techniques  
Through machine learning techniques such as Adaboost, it is possible to identify patterns in data that can be used for clinical data analysis. [PERSON] et al. [3] performed a two-stage analysis to examine the attributes and classifications of patients with heart failure. The results of the study revealed that, although the system had only 13 attributes, it still retained plenty of information about the disease. The data collected by various classifiers were analyzed. Adaboost with a Decision Tree (DT) achieved the highest performance across all datasets. However, there were a few concerns with the data distribution. [PERSON] et al. [4] developed a system that can diagnose heart diseases using Fuzzy Logic and a Genetic Algorithm. A Genetic Algorithm was used to solve the feature selection problem. The data collected during the course of the study were then used to develop a fuzzy inference system. Their work used a Genetic Algorithm to select the relevant subset of rules for predicting heart disease in patients. Some of the key features that could be predicted using this algorithm included sex, serum cholesterol, exercise-induced angina, and depression [5; 6]. This work was evaluated using various performance metrics, such as accuracy, sensitivity, and specificity, to evidence the efficiency of the system. The proposed system achieved an accuracy of 86% through a stratified k-fold technique that used the values for sensitivity and specificity. The proposed model, which was called Genetic Algorithm Fuzzy Logic, is commonly used in hospitals and medical centers. The various forms of data collected from a patient's medical history can be used to predict the risk of heart disease. To remove uncertainty from the data, a membership function was introduced that allowed users to customize the data collected. [PERSON] et al. [7] used a minimum-distance K-NN classifier and discovered that the Fuzzy K-NN classifier performed well compared to other parametric model-based classifiers. A similar system was developed by [PERSON] et al. [8] that used a fuzzy rule base to predict heart diseases. It was able to achieve an accuracy of approximately 80%. The model was constructed using a modified differential evolution method, Analytic Hierarchy Process(AHP), and a Feedforward Neural Network (FNN). The model with the most important features was then chosen, and the attributes were then fed into the network. [PERSON] et al. [9] further optimized the model to predict heart disease using fuzzy AHP with an FNN. The proposed method was formulated to reduce computational time and improve accuracy. It was tested and achieved an accuracy of 84.16% [10]. [PERSON] et al. [11] presented hybrid data-mining techniques in 2017. This study showed that these techniques can be used to extract valuable information from large amounts of data by taking advantage of the various attributes of the data. A back-propagation algorithm was proposed to predict the likelihood of a person developing heart disease. It used various medical terms such as blood pressure, cholesterol, and sex to identify potential risk patients [12]. The proposed heart disease risk prediction system consisted of two stages: the mechanized development of rules, and the building up of a fuzzy principle based on a genetic algorithm. The framework is then built based on weighted fuzzy standards. Subsequently, it can identify the most appropriate risk level for a given patient. The goal of this study was to help non-specialized doctors to make the right choice regarding the risk level of a patient with coronary illness [13]. [PERSON] et al. [14] proposed a system for predicting heart disease using various techniques, such as Fuzzy Logic, Decision Trees, Support Vector Machines (SVM), Artificial Neural Networks, and Adaboost. The proposed system was evaluated using the Least Absolute Shrinkage and Selection Operator (LASSO), feature selection, and mRMR.