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Heart Failure Development Prediction using Stochastic Gradient Descent Optimization
2
Zitationen
3
Autoren
2022
Jahr
Abstract
More than 64 million people in the world suffer from heart failure. Automated diagnostic methods using machine learning are an effective tool to detect the disease at an early stage. The study aims to build a prediction model for patients with suspected heart failure based on the linear regression model optimized with stochastic gradient method. An open dataset of patients with suspected heart failure was used for the experimental study, including 1504 records with 13 attributes. The resulting model has sufficient performance indicators for its use in clinical practice for predicting the development of heart failure in the early stages.
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