# Detection of Arrhythmias Using Heart Rate Signals from Smartwatches
## 3 Materials and Methods
### Model Configuration  
The data set comprised tabular data; therefore, machine learning and deep learning algorithms were chosen for training and testing on the data. We refer to studies on commonly used machine learning algorithms and deep learning techniques [47; 48] to provide descriptions of the algorithms used in the experiments:  
1. A multi-layer perceptron (MLP) is a kind of artificial neural network made up of an input layer, one or more hidden layers, and an output layer, among other layers. A completely linked network is formed when every neuron in one layer is coupled to every other layer's neurons. Because MLPs can learn intricate, non-linear correlations from data, they are useful for a wide range of tasks, including natural language processing and picture identification. The sole deep learning algorithm that we employed in our research was an MLP. 2. A support vector machine (SVM) is an effective classifier that divides data into two classes by determining the ideal hyperplane. The best hyperplane optimizes the margin--also referred to as support vectors--between the closest data points belonging to distinct classes. With the addition of kernel functions, support vector machines (SVMs) can be expanded to handle non-linear classification tasks. SVMs are especially efficient in high-dimensional fields. 3. Logistic regression is a statistical technique applied to situations involving binary categorization. It uses a logistic function to represent the likelihood that a sample will belong to a specific class; the logistic function returns a number between 0 and 1. Logistic regression is a popular technique for managing huge data sets with a clear decision boundary because it is easy to interpret and efficient, despite its simplicity. Figure 2: Model generation pipeline. 4. A decision tree is a non-parametric supervised learning technique applied to regression and classification. A tree-like decision model is produced by segmenting the data into subsets according to the most important feature. Decision trees can be prone to over-fitting even if they are simple to understand and display, particularly when dealing with noisy data. 5. A random forest is a technique for group learning that builds several decision trees during training and produces a class that is the average of the classes produced by the individual trees. By averaging the predictions, this improves accuracy and decreases over-fitting. Because of their great adaptability, random forests may be applied to tasks involving both regression and classification. 6. Using gradient boosting, models are constructed one after the other, with each new model aiming to fix the flaws in its predecessor. Because it concentrates on lowering the model's loss function, it is very successful for predictive modeling jobs. Popular versions include LightGBM, which is memory- and speed-optimized; CatBoost, which natively handles categorical features; and XGBoost, which is renowned for its accuracy and efficiency. 7. Extreme gradient boosting (XGB) is an effective and scalable use of gradient boosting. It is an excellent option for structured or tabular data since it incorporates a number of optimizations, including regularization strategies, sparsity awareness, and parallelized tree construction. 8. Light gradient boosting machine (LGBM) is an additional gradient boosting framework with a very low-memory- and computational-cost design. It grows trees in a leaf-wise manner, which may lead to deeper trees and possibly higher accuracy. LGBM is renowned for its quick training speed and is especially helpful when handling huge data sets. 9. CatBoost is a gradient boosting approach that does not require explicit pre-processing, like one-hot encoding, to handle categorical information. It is useful for many real-world applications because it reduces over-fitting and enhances model generalization through the use of a unique method known as ordered boosting. 10. A straightforward instance-based learning approach for regression and classification is the \(k\)NN algorithm.