Application of quantitative analysis of biomechanical data in predicting healthcare investment trends

Article ID: 1156

Abstract

Traditional methods for predicting investment trends often rely on macroeconomic data, overlooking the influence of individual biomechanical characteristics on decision-making, particularly in the health and medical fields. This paper seeks to enhance the accuracy of healthcare investment trend predictions by integrating high-precision biomechanical data acquisition technology with advanced quantitative analysis methods. High-precision sensors and smart wearable devices are employed to collect individual biomechanical data, encompassing dynamic features such as sports performance, joint angles, and gait. To ensure data quality, a rigorous preprocessing procedure is implemented. Principal component analysis (PCA) is utilized for feature extraction, minimizing redundant information and isolating the most representative biomechanical features. During the data analysis phase, a hybrid model combining random forests and support vector machines (SVM) is employed to predict healthcare investment trends. Random forests are applied for feature selection and regression analysis, while SVMs address classification tasks for trend prediction. The results indicate that the proposed model achieves an accuracy and precision exceeding 0.9, with healthcare investment returns on investment (ROI) ranging from 20% to 50%. The findings underscore the potential of biomechanical data in providing valuable insights for healthcare investment trend predictions, ultimately driving innovation and progress in the industry.