Written by : Jayati Dubey
January 20, 2025
The researchers noted that the healthcare sector generates vast amounts of data, offering valuable opportunities for applying AI to address real-life health challenges.
Researchers from Hyderabad-based Woxsen University, in collaboration with US scientist Prof Manjeet Rege, have developed an artificial intelligence-based prediction model to assess obesity risk.
The team includes Bobba Bharath Reddy, Dr. Hemachandran Kannan, and Dr. Shahid Mohammad Ganie, who explored the use of machine learning techniques to analyze lifestyle data for predicting obesity.
The researchers noted that the healthcare sector generates vast amounts of data, offering valuable opportunities for applying AI to address real-life health challenges.
By combining multiple machine learning methods, the team aimed to create a more accurate prediction model to assess obesity risk.
They selected three algorithms from each ensemble method, leveraging their distinct characteristics to demonstrate the model's effectiveness from multiple perspectives.
This approach allows for a more comprehensive understanding of obesity risk factors.
The study highlights the limitations of using body mass index (BMI) as the sole indicator of obesity risk.
While BMI is widely used, it does not consider critical health factors such as muscle mass, fat distribution, and other variables, which can influence obesity classification.
By incorporating behavioral, environmental, and genetic factors, the proposed model addresses these gaps for improved precision.
The findings were published in a paper titled Investigation of Ensemble Learning Techniques for Obesity Risk Prediction Using Lifestyle Data in the Decision Analytics Journal by Elsevier.
This research showcases how AI can contribute to addressing complex health challenges.
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