Machine Learning: a New Opportunity for Risk Prediction
نویسندگان
چکیده
منابع مشابه
CS229 Project: A Machine Learning Approach to Stroke Risk Prediction
In this paper, we consider the prediction of stroke using the Cardiovascular Health Study (CHS) dataset. Missing data imputation, feature selection and feature aggregation were conducted before training and making prediction with the dataset. Then we used a mixture of support vector machine (SVM) and stratified Cox proportional hazards model to predict the occurrence of stroke. Different method...
متن کاملNew Machine Learning Methods for the Prediction of Protein Topologies
Protein structures are translation and rotation invariant. In protein structure prediction, it is therefore important to be able to assess and predict intermediary topological representations, such as distance or contact maps, that are translation and rotation invariant. Here we develop several new machine learning methods for the prediction and assessment of fine-grained and coarse topological...
متن کاملMachine Learning for Traffic Prediction
Using machine learning for predicting traffic is described in the context of a competition organized using the TunedIT platform. A heuristic is proposed for reconstructing the route of a car in a street graph from a temporal stream of its coordinates. A resilient propagation neural network for approximating the average velocity on a given street from irregular time series of instantaneous veloc...
متن کاملA Hybrid Machine Learning Method for Intrusion Detection
Data security is an important area of concern for every computer system owner. An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Already various techniques of artificial intelligence have been used for intrusion detection. The main challenge in this area is the running speed of the available implemen...
متن کاملTransparent Machine Learning Algorithm Offers Useful Prediction Method for Natural Gas Density
Machine-learning algorithms aid predictions for complex systems with multiple influencing variables. However, many neural-network related algorithms behave as black boxes in terms of revealing how the prediction of each data record is performed. This drawback limits their ability to provide detailed insights concerning the workings of the underlying system, or to relate predictions to specific ...
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: Korean Circulation Journal
سال: 2020
ISSN: 1738-5520,1738-5555
DOI: 10.4070/kcj.2019.0314