Machine Learning Holds Promise in Predicting Canadians’ Future Health
Researchers at the University of Alberta are harnessing the power of machine learning to predict the future mental and physical health of Canadians. Led by associate professor Cloud Cao, the team aims to leverage machine learning algorithms to make accurate predictions about individuals’ health conditions and outcomes as they age. These predictions could potentially aid doctors in making informed decisions about patient care, ultimately improving overall health outcomes.
The team recently published two studies related to predicting future health in Canada. The first study focused on developing a biological age index, which compares a person’s chronological age with their BioAge. The BioAge is determined by analyzing blood markers and serves as an indicator of overall health. Poor lifestyle choices, like smoking, can result in a positive BioAge, leading to significant health challenges. Conversely, healthy lifestyle choices, such as regular exercise, can result in a negative BioAge, indicating better health.
The study aimed to identify the factors that influence a positive or negative BioAge. The research team incorporated various variables, including lifestyle choices, social economics, and cognitive function. By understanding which factors are most significant, healthcare professionals can tailor treatments and interventions to address specific areas of concern.
The second study delved into predicting the onset of depression within three years. Researchers collected baseline data, such as personality measures and self-perceived health, and then conducted follow-ups to determine if they could predict future depression onset based solely on this data. The team developed a machine learning model that achieved roughly 70% accuracy in forecasting depression development within the specified timeframe.
While these research studies show promising results, implementing machine learning for predicting future health in Canada is still a work in progress. Cao emphasizes the need for more data, a larger population sample, and the inclusion of additional factors to further improve the accuracy of these models. The ultimate goal is to develop prototypes that can be effectively utilized beyond the research domain and bring tangible benefits to Canadians.
As more advancements are made in the field of machine learning, healthcare professionals envision a future where predictive models can help individuals make better choices and healthcare providers deliver more personalized and effective care. By leveraging the power of artificial intelligence and data analysis, researchers like Cloud Cao are paving the way for a new era of healthcare that prioritizes prevention and prediction, ultimately improving the overall well-being of Canadians.
