Computational Intelligence Techniques for Assessing Anthropometric Indices Changes in Female Athletes.

Area of Science:

• Sports Science
• Nutritional Science
• Computational Intelligence

Background:

• Athlete performance is influenced by physical characteristics.
• Current slimming estimation models have limitations in accuracy and time efficiency.
• Accurate prediction of body composition changes is crucial for athletes.

Purpose of the Study:

• To determine the slimming effect of a herbal composition (caraway extract) on female athletes.
• To develop a novel, accurate prediction model for anthropometric changes.
• To evaluate the efficacy of caraway extract as a natural weight loss aid.

Main Methods:

• A hybrid adaptive network-based fuzzy inference system and particle swarm optimization (ANFIS-PSO) model was developed.
• The model predicted changes in waist circumference, waist-to-hip ratio, thigh circumference, and mid-upper arm circumference.
• A ninety-day clinical trial involving female athletes consuming caraway extract was conducted.

Main Results:

• Caraway extract significantly reduced all measured anthropometric indices in female athletes.
• The ANFIS-PSO model demonstrated higher accuracy in predicting these changes compared to SPSS.
• The proposed hybrid approach's efficiency was validated with existing data.

Conclusions:

• Hybrid adaptive neuro-fuzzy techniques offer improved predictive accuracy and simplification for body characteristic analysis.
• These advanced methods are more valid and useful for clinical applications than conventional analytical techniques.
• Caraway extract shows potential as a natural intervention for weight management in athletes.