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Publication Type : Journal Article
Publisher : IEEE Access
Source : IEEE Access, vol. 9, pp. 99768-99780, 2021.
Campus : Bengaluru
School : School of Engineering
Department : Electronics and Communication
Year : 2021
Abstract : With the advent of smart systems and smart IoT network all over the world leading to enormous amount of data generation; the right analysis and decision making based on the relevant data plays a crucial role. Various industries such as transportation, retail, healthcare etc. rely on analysis using this huge volumes of data for intelligent decision making. In smart healthcare system, accurate analysis of patients' data and prediction of diseases and medicine is important. To a great extent, fatalities can be avoided by timely recommendation of healthcare measures and immediate alert on emergency conditions. The use of machine learning algorithm for precise predictive analysis of data can be very promising in the field of healthcare. In this paper, optimised Multivariable Linear regression method is used to predict the diabetic disease progression of 442 patients based on various parameters such as age, gender, Body Mass Index and 6 different blood serum measurements. Here optimisation is performed using feature reduction and logarithmic transformation. The predicted output is found to be closely associated with actual output data with a Root Mean Square Error of 1.5 units; which indicates higher accuracy in comparison with the non-optimised model with the error of 54 units. There has also been a comparison with the results obtained from other state of the art regression methods, which proves that the proposed model exhibits maximum accuracy. This method can be used to provide promising medical advice to the patients on how to reduce the diabetic disease progression over a year by controlling various health parameters.
Cite this Research Publication : V. K. Daliya, T. K. Ramesh and S. -B. Ko, "An Optimised Multivariable Regression Model for Predictive Analysis of Diabetic Disease Progression," in IEEE Access, vol. 9, pp. 99768-99780, 2021, doi: 10.1109/ACCESS.2021.3096139.