Logistic Regression Models in Predicting Heart Disease
نویسندگان
چکیده
منابع مشابه
Logistic Regression: Predicting Counts
For the most part, this book concerns itself with measurement data and the corresponding analyses based on normal distributions. In this chapter and the next we consider data that consist of counts. Elementary count data were introduced in Chapter 5. Frequently data are collected on whether or not a certain event occurs. A mouse dies when exposed to a dose of chloracetic acid or it does not. In...
متن کاملPredicting amphipod toxicity from sediment chemistry using logistic regression models.
Individual chemical logistic regression models were developed for 37 chemicals of potential concern in contaminated sediments to predict the probability of toxicity, based on the standard 10-d survival test for the marine amphipods Ampelisca abdita and Rhepoxynius abronius. These models were derived from a large database of matching sediment chemistry and toxicity data, which includes contamina...
متن کاملPredicting Early Transplant Failure: Neural Network Versus Logistic Regression Models
Cox’s proportional hazard model or logistic regression model has been the classical mathematical approach to predict transplant results, but artificial neural networks may offer better results. In order to compare both methods, a logistic regression and a neural network model were generated to predict early transplant failure assessed at 90 days. Methods: Medical charts from 701 liver transplan...
متن کاملComparison of logistic regression and neural network models in predicting the outcome of biopsy in breast cancer from MRI findings
Background: We designed an algorithmic model based on the logistic regression analysis and a non-algorithmic model based on the Artificial Neural Network (ANN). Materials and methods: The ability of these models was compared together in clinical application to differentiate malignant from benign breast tumors in a study group of 161 patients' records. Each patient’s record consisted of 6 subjec...
متن کاملPredicting shrimp disease occurrence: artificial neural networks vs. logistic regression
Predicting the occurrence of disease outbreaks in aquacultural farms can be of considerable value to the long-term sustainable development of the industry. Prior research on disease prediction has essentially depended upon traditional statistical models with varying degrees of prediction accuracy. Furthermore, the application of these models in sustainable aquaculture development and in control...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Physics: Conference Series
سال: 2021
ISSN: 1742-6588,1742-6596
DOI: 10.1088/1742-6596/1769/1/012024