Exploring the feasibility of near-infrared spectroscopy and machine learning for detecting cardiovascular diseases and diabetes mellitus in fingernails

Key Points

• The research aims to evaluate the use of near-infrared spectroscopy combined with machine learning for detecting cardiovascular diseases and diabetes.
• Utilized near-infrared spectroscopy to capture disease-related spectral regions.
• Employed machine learning classification models for analysis.
• Analyzed glycated products in fingernails to assess disease risk.
• Achieved high levels of predictive accuracy in detecting diseases.
• Demonstrated potential for early diagnosis using fingernail analysis.