Abstract
The development of a health evaluation system from human-related data is an important issue in preventive medicine. Previously, most studies have focused on disease assessment and prevention in patients. However, even if certain risk factors are all within normal ranges, individuals may not necessarily be completely healthy. This study focused on healthy individuals to develop a new index to assess health risks; this index can be used for the prevention of multiple diseases in healthy people. The kernel density technique was proposed to estimate the distribution of common risk factors and to develop a health risk index. A dataset of hypertension, hyperlipidemia, and hyperglycemia (Triple H) data from the National Health Insurance Research Database in Taiwan was used to demonstrate the proposed analytical process. The results of risk factor changes after six weeks of exercise were used to calculate the health risk index. The results showed that the subjects experienced a 7.29% reduction in their health risk index after the exercise intervention. This finding demonstrates the potential impact of an important reference index on quantifying the effect of maintenance in healthy people.
Highlights
For a long time, the diagnosis and treatment of diseases have been critical aspects of medical development
This study proposed a novel three-stage analysis procedure involving the feature selection method, This study proposed a novel three-stage analysis procedure involving the feature selection the kernel density estimation method, and mathematical approaches to calculate the health risk index method, the kernel density estimation method, and mathematical approaches to calculate the health of healthy people
This study evaluated six types of common kernel functions, including Gaussian, Epanechnikov, Triangular, Uniform, Bright, and Cosine, to estimate the kernel density values for all the common risk factors
Summary
The diagnosis and treatment of diseases have been critical aspects of medical development. Many scholars have used data mining techniques on medical data to analyze the relationships between disorders and the real causes of those disorders [1,2,3,4]. In recent years, determining the common risk factors and developing a predictor model for multiple diseases have become more important. Chang et al [8] proposed a two-stage analysis procedure that used data mining techniques and mathematical approaches to determine the common risk factors (such as systolic blood pressure (SBP), triglycerides (TGs), uric acid, glutamate pyruvate transaminase, and gender) and predictive models for hypertension and hyperlipidemia. Medical decision systems, based on analyzing risk factors and predicting the functions of diseases, can help patients understand the risks of developing diseases and efficiently provide diagnostic references for medical personnel. The rapid development of medical devices has made it easy to
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