Original title: CAPCODRE: A Computational Systems Biology and Machine Learning-Based Approach to Predict Cognitive Disorder Risk in the Elderly
Authors: Srilekha Mamidala
As global life expectancy increases, so does the population of elderly individuals. Unfortunately, with age comes a higher risk of cognitive impairment, including diseases like Alzheimer’s. In response, researchers have developed a new algorithm, called CAPCODRE, to predict the risk of developing cognitive disorders in the elderly. CAPCODRE combines machine learning and systems biology approaches, using data on environmental pollution and gene-protein interactions, as well as hospitalization records for cognitive impairment. The algorithm was trained and optimized using various techniques and was able to successfully predict and model the risk of cognitive health issues with a high level of accuracy. To make this information easily accessible, the algorithm was integrated into a user-friendly app, allowing individuals to receive personalized predictions based on their medical history and location. CAPCODRE not only sheds light on the impact of environmental pollution on cognitive health, but also has the potential to address racial disparities in diagnosis and treatment of cognitive disorders, promoting more equitable and accessible care for all.
Original article: https://arxiv.org/abs/2311.09229