Obesity can lead to several lifestyle diseases. Reducing weight to the optimum level can help prevent several diseases. Once someone becomes obese, it is tough to lose weight. If someone knows about the risk of becoming obese, they can take corrective measures.

Woxsen University Researchers, along with US scientists, developed an AI-based prediction model to detect what they call ‘Obesity Risk’.

The team, which includes Shahid Mohammad Ganie, Hemachandran Kannan, and Bobba Bharath Reddy, along with Manjeet Rege, a professor at the University of St. Thomas, USA, have explored how combining multiple machine learning methods can predict the risk of obesity based on lifestyle data.

They introduced an AI-based prediction model that leverages boosting techniques to assess the risk of obesity more effectively.

Their research paper, ‘An Investigation of Ensemble Learning Techniques for Obesity Risk Prediction Using Lifestyle Data,’ was published in the Decision Analytics Journal by Elsevier. 

“Identifying the underlying causes of obesity risk in its early stages has become a challenge for medical practitioners,” it said.

“Our goal has always been to use technology to create meaningful solutions to real-world problems. By focusing on lifestyle factors and ensemble learning methods, we aim to provide healthcare professionals with actionable insights while empowering individuals to make informed decisions about their well-being,” Hemachandran Kannan said.

The researchers observed that while BMI (body mass index) is commonly used as the primary indicator of obesity risk, it has limitations in capturing the full complexity of obesity, which is influenced by behavioural, environmental, and genetic factors.

“They noted that BMI does not account for critical health indicators such as muscle mass, fat distribution, or other variables, potentially reducing the precision of obesity classification,” they argued.

“Our study highlighted that while some machine learning models for obesity classification incorporate lifestyle factors, sex, and 3D body scans without relying solely on BMI, the datasets used in these models often stem from smaller sample sizes in specific regions or countries,” they said.

This limited scope restricts the models’ ability to generalise findings to broader or global populations.

The study also noted that the perfect climate for obesity to flourish has been created by traditional diets heavy in processed foods and low physical activity as technology develops and urbanisation picks up speed. The resultant effects include an increase in disorders linked to obesity, such as diabetes, heart problems, and other ailments.

The researchers utilised a publicly available dataset drawn from diverse populations in countries like Colombia, Peru, and Mexico. The dataset incorporated factors such as eating habits, age, sex, physical condition, water and alcohol consumption, and the frequency of vegetable intake.

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