Author(s)

Soniya Lalwani, M. Krishna Mohan, Pooran Singh Solanki, Sorabh Singhal, Sandeep Mathur, Emilio Spedicato

Advanced Bioinformatics Centre, Birla Institute of Scientific Research, Jaipur, India.
Department of Endocrinology, SMS Medical College and Hospital, Jaipur, India.
Department of Operation Research, University of Bergamo, Bergamo, Italy.

Linear Least Square (LLS) is an approach for modeling regression analysis, applied for prediction and quantification of the strength of relationship between dependent and independent variables. There are a number of methods for solving the LLS problem but as soon as the data size increases and system becomes ill conditioned, the classical methods become complex at time and space with decreasing level of accuracy. Proposed work is based on prediction and quantification of the strength of relationship between sugar fasting and Post-Prandial (PP) sugar with 73 factors that affect diabetes. Due to the large number of independent variables, presented problem of diabetes prediction also presented similar complexities. ABS method is an approach proven better than other classical approaches for LLS problems. ABS algorithm has been applied for solving LLS problem. Hence, separate regression equations were obtained for sugar fasting and PP severity.

ABS Algorithm; Linear Least Square; Regression; Diabetes; Huang Algorithm

S. Lalwani, M. Mohan, P. Solanki, S. Singhal, S. Mathur and E. Spedicato, "An Application of the ABS Algorithm for Modeling Multiple Regression on Massive Data, Predicting the Most Influencing Factors," Applied Mathematics, Vol. 4 No. 6, 2013, pp. 907-913. doi: 10.4236/am.2013.46126.