Abstract
This article uses linear regression in machine learning and LightGBM algorithm for data mining, and compares the linear regression and LightGBM algorithm’s ability of fast running speed and high accuracy under the same data conditions. The least squares method is used to make error judgments to realize the rapid and accurate prediction and judgment of the probability of diabetes in a large amount of data. At the same time, the importance and correlation of the variables are compared between the variables. Among them, it is found that Body Mass Index (BMI) has the greatest impact on diabetes, and the factors affecting BMI are weight and height. Height and age can be input into system, automatically determining the BMI value, and providing doctors with a basis for judgment.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
