# Taylor Bird Swarm Algorithm Based on Deep Belief Network for Heart Disease Diagnosis
## 2 Motivations
### Literature Survey  
[PERSON], [PERSON] et al. [22] devised an adaptive genetic algorithm with fuzzy logic (AGAFL) model for predicting heart disease, which assists clinicians in treating heart disease at earlier phases. The model comprises rough sets with a fuzzy rule-based classification module and heart disease feature selection module. The obtained rules from fuzzy classifiers are optimized by adapting an adaptive genetic algorithm. Initially, the significant features that affect heart disease are chosen using the rough set theory. Then, the second step predicts heart disease with the AGAFL classifier. The method is effective in handling noisy data and works effectively with large attributes. [PERSON] et al. [23] devised a method for selecting features and reducing the number of features.  
Here, the imperialist competitive algorithm was devised to choose important features from heart disease. This algorithm offers an optimal response in selecting features. Moreover, the k-nearest neighbor algorithm was utilized for classification. The method showed that the accuracy of feature selection was enhanced. However, the method failed to utilize incomplete or missed data. [PERSON] and [PERSON] [26] devised a model using Cleveland heart samples for heart disease diagnosis. The method employed cluster-based Decision Tree learning (CDTL) for diagnosing heart disease. Here, the original set was partitioned using target label distribution. From elevated distribution samples, the possible class was derived. For each class set, the features were detected using entropy for diagnosing heart disease. [PERSON] and [PERSON] [27] developed Particle Swarm Optimization and Rough Sets with Transductive Support Vector Machines (PSO and RS with TSVM) for heart disease diagnosis. This method improved data integrity to minimize data redundancy. The normalization of data was carried out using Zero-Score (Z-Score). Then, the PSO was employed for selecting the optimal subset of attributes, reduce computational overhead, and enhance prediction performance. The Radial Basis Function-Transductive Support Vector Machines (RBF-TSVM) classifier was employed for heart disease prediction. [PERSON], [PERSON] et al. [28] devised a model using Internet of Things (IoT) for determining and monitoring heart patients. The goal of the healthcare model was to obtain improved precision for diagnosis. The neutrosophic multi-criteria decision-making (NMCDM) technique was employed for aiding patients (i.e., for observing patients suffering from heart failure). Moreover, the model provided an accurate solution that decreases the rate of death and the cost of treatment. [PERSON], [PERSON] et al. [24] devised a predictive technique for heart disease diagnosis with machine learning models. Here, the method adapted unsupervised and supervised learning for diagnosing heart disease. In addition, the method employed Self-Organizing Map, Fuzzy Support Vector Machine (FSVM), and Principal Component Analysis (PCA) for missing value assertion. Moreover, incremental PCA and FSVM are devised for incremental learning of data to minimize the time taken for computation in disease prediction. [PERSON], [PERSON] et al. [29] devised an automatic diagnostic technique for diagnosing heart disease. The method evaluated the pertinent feature subset by employing the benefits of feature selection and extraction models. For accomplishing the feature selection, two algorithms: accuracy based feature selection algorithm (AFSA) and Mean Fisher based feature selection algorithm (MFFSA) for heart disease diagnosis. However, the method failed to employ PCA for dimension reduction. [PERSON], [PERSON] [25] devised a hybrid method for diagnosing heart disease. The method combined the cuckoo search (CS) and rough set to infer decision rules. Moreover, the CS was employed for discovering essential features. In addition, three major features were evaluated with rough set rules. The method improved feasibility, but failed to induce an intuitionistic fuzzy rough set and CS for diagnosing heart disease.