Simple Method to Predict Insulin Resistance in Children Aged 6-12 Years by Using Machine Learning

Area of Science:

• Pediatric Endocrinology
• Biostatistics
• Machine Learning in Healthcare

Background:

• Childhood insulin resistance (IR) is a growing concern, potentially leading to increased rates of diabetes and cardiovascular disease.
• Early identification of IR in children is crucial for timely intervention and preventing long-term health complications.
• Primary care practitioners require accessible tools to screen for pediatric IR.

Purpose of the Study:

• To develop and validate a machine learning model for predicting insulin resistance in children aged 6-12 years.
• To identify key clinical features predictive of IR in the pediatric population.
• To establish a simple, cost-effective method for IR screening in primary care settings.

Main Methods:

• Utilized data from 503 children (aged 6-12), defining IR using HOMA-IR > 3.0.
• Applied data preprocessing techniques including imputation and oversampling, followed by recursive feature elimination.
• Trained and evaluated five machine learning models (LR, SVM, RF, XGBoost, CatBoost) on an external dataset of 133 children.

Main Results:

• The XGBoost model, utilizing 14 selected features including glucose, waist circumference, and age, demonstrated the highest performance.
• XGBoost achieved an Area Under the Curve (AUC) of 0.85, with strong accuracy, sensitivity, precision, and F1 scores.
• XGBoost exhibited superior specificity (0.79) compared to other models, making it the optimal choice for IR prediction.

Conclusions:

• The developed XGBoost model accurately predicts IR in children aged 6-12.
• This model offers a clinically applicable, simple, and inexpensive method for pediatric IR screening.
• Early identification via this model can facilitate timely interventions, mitigating risks of future diabetes and cardiovascular disease.