A multiple linear regression model for predicting characteristic frequencies in biological tissues

• Published in: AIP advances 2024-11, Vol.14 (11), p.115013-115013-11
• Main Authors: Apon, Imtiaz Ahamed, Hasan, Md. Ratul, Zafur, Abu, Wahid, Md Ferdoush, Haque, Mohammad Salman
• Format: Article
• Language: English
• Subjects: Bias; Bioelectricity; Biological models (mathematics); Body size; Cardiovascular system; Data points; Dengue fever; Error analysis; Females; Human tissues; Impedance; Males; Regression models; Root-mean-square errors; Statistical analysis
• ISSN: 2158-3226; 2158-3226
• DOI: 10.1063/5.0237567

This research introduces a novel mathematical methodology for identifying the distinctive frequency of human tissue. The model has been formulated using bioelectrical impedance analysis. The developed model can be utilized to detect a range of ailments, including those associated with the cardiovascular system, cancer, and dengue fever. A total of 3813 data points, including both males and females, were utilized. Data from a sample of both male and female individuals, including their age, height, bioelectrical impedance at frequencies ranging from 5 kHz to 1 MHz (for the Fc model), body mass index, and an impedance index of 2000, were utilized to create mathematical models. To validate the suggested models, data from a total of 1813 individuals (both male and female) were utilized. The statistical analysis of the proposed model (Fc) reveals a significant correlation (Pearson coefficient = 0.997, p < 0.001) between both male and female subjects, with a positive covariance. The model’s 95% limits of agreement, ranging from −1.28 to 1.98 L for both males and females, are sufficiently minimal. All errors fall within this limit. In addition, the suggested model has undergone validation in terms of various types of error analysis, such as bias and root mean square (RMSE). The bias and RMSE values, which are indicators of error, reach a maximum of 0.32 and 0.38 L (for both male and female), respectively. These values are within the predicted range and can be considered minimal.