One of the developments in machine learning is the technology that has been used for disease prediction in many fields around the world, including the healthcare industry. Analysis has been attempted to classify the most influential heart disease causes and to reliably predict the overall risk ...
27 have proposed a framework of a hybrid system for the identification of cardiac disease, using machine learning, and attained an accuracy of 86.0%. Similarly, Mohan et al.28 have proposed another intelligent system that integrates RF with a linear model for the prediction of heart disease and...
Based on the prediction accuracy reported here, we designed a potentially realistic prediction–surveillance selection process in an example real-world population of 1 million patients with available longitudinal EHRs. The analysis indicates that, by using an ML model trained on all data to predict th...
Prediction of Cognitive Impairment Using Sleep Lifelog Data and LSTM Model Rapid elderly population growth has increased the number of patients with cognitive impairment (CI). Early detection and ongoing medical treatment can slow CI progression and significantly reduce the cost of managing patients. How...
To run this reference kit, first clone this repository, which can be done using git clone https://www.github.com/oneapi-src/disease-prediction This reference kit implementation already provides the necessary scripts to setup the above software requirements. To utilize these environment scripts, first...
Currently, people with Parkinson’s are treated with dopamine replacement therapy after they have already developed symptoms, such as tremor, slowness of movement and gait, and memory problems. However, researchers believe that early prediction and diagnosis would be valuable for finding treatments ...
et al. Prediction of freezing of gait in Parkinson’s disease using wearables and machine learning. Sensors 21, 614 (2021). Article ADS PubMed PubMed Central Google Scholar Prado, A., Kwei, S. K., Vanegas-Arroyave, N. & Agrawal, S. K. Continuous identification of freezing of gait ...
Predicting the effects of coding variants is a major challenge. While recent deep-learning models have improved variant effect prediction accuracy, they cannot analyze all coding variants due to dependency on close homologs or software limitations. Here
In part 1 of the 2-part Intelligent Edge series, Bharath and Xiaoyong explain how data scientists can leverage the Microsoft AI platform and open-source deep learning frameworks like Keras or PyTorch to build an intelligent disease prediction deep learni
Predicting disease candidate genes from human genome is a crucial part of nowadays biomedical research. According to observations, diseases with the same phenotype have the similar biological characteristics and genes associated with these same diseases tend to share common functional properties. Therefore,...