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Machine Learning-Based Rapid Prediction of Sudden Cardiac Death (SCD) Using Precise Statistical Features of Heart Rate Variability for Single Lead ECG Signal
Published in Sourav Banerjee, Chinmay Chakraborty, Kousik Dasgupta, Green Computing and Predictive Analytics for Healthcare, 2020
Logistic regression analysis studies the association between a categorical dependent variable and a set of independent variables. The name logistic regression is used when the dependent variable has only two values, such as 0 and 1 [27]. Logistic regression, sometimes called the logistic model or logit model, analyzes the relationship between multiple independent variables and a categorical dependent variable, and estimates the probability of occurrence of an event by fitting data to a logistic curve [27,28]. Logistic regression is a method for fitting data to a regression curve, y=f(x), when y consists of binary coded data [28]. When the response is a binary variable, and x is numerical, logistic regression fits a logistic curve to the relationship between x and y. A logistic curve is an S-shaped or sigmoid curve, often used to model population growth, and is represented in Figure 4.6. A logistic curve starts with slow, linear growth, followed by exponential growth, which then slows again to a stable rate. A simple logistic function is defined by the formula. A decision boundary is a pretty simple concept. Logistic regression is a classification algorithm which has some decision such as Yes/No, True/False, Red/Yellow/Orange [28]. Our prediction function, however, returns a probability score between 0 and 1. A decision boundary is a threshold or tipping point that helps to determine which category to choose based on probability [28]. For example, if the threshold is .5 and prediction function is .7, then observation is positive. If the prediction was .2, then it is classified as negative. For logistic regression with multiple classes, it could be to select the class with the highest predicted probability. If p≥0.5, then it is treated class=1(probability is denoted p)
Classification of Customer Reviews Using Machine Learning Algorithms
Published in Applied Artificial Intelligence, 2021
SVM is a supervised learning method used for categorization. It is a useful methodology that finds the best possible surface to separate the positive samples from the negative samples. The basic goal of SVM, behind the training process, is to find a maximum margin hyperplane to solve the feature review’s classification task. There are unlimited possible boundaries to separate the two classes. To select the best class, it is important to choose a decision boundary that has a maximum margin between any points from both classes. The decision boundary with a maximum margin would be less likely to make prediction errors, which is close to the boundaries of one of the classes (Ali, Kwak, and Kim 2016). The dot kernel type was selected because of its better performance against radial and polynomial kernel types. The C parameter set 2.
Predicting Breast Density of Digital Breast Tomosynthesis from 2D Mammograms
Published in IETE Journal of Research, 2023
Jinn-Yi Yeh, Tu-Liang Lin, Siwa Chan
Decision trees are mainly used for classification. The method is to split the data into several subsets, and each subset can only belong to one category. Graphically, this is to divide the input data into regions of the same category as much as possible. The boundary separating these areas is called the decision boundary. The decision tree model makes classification decisions based on these decision boundaries. A decision tree is a hierarchical structure with nodes and branches. The top node is called the root node. The nodes at the bottom are called leaf nodes. A node that is neither a root node nor a leaf node is called an internal node.