Use and performance of machine learning models for type 2 diabetes prediction in community settings: A systematic review and meta-analysis

K De Silva, WK Lee, A Forbes, RT Demmer… - International journal of …, 2020‏ - Elsevier
Objective We aimed to identify machine learning (ML) models for type 2 diabetes (T2DM)
prediction in community settings and determine their predictive performance. Method …

A survey on event detection approaches for sensor based IoT

M Kumar, PK Singh, MK Maurya, A Shivhare - Internet of Things, 2023‏ - Elsevier
Over the period, two significant advancements ie shrinking of the sensor size and improved
communication capability, lead the sensor-based internet-of-things (SBIoT) to mature …

Architecting smart city digital twins: Combined semantic model and machine learning approach

M Austin, P Delgoshaei, M Coelho… - Journal of Management …, 2020‏ - ascelibrary.org
This work was motivated by the premise that next-generation smart city systems will be
enabled by widespread adoption of sensing and communication technologies deeply …

Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm

R Kamalraj, S Neelakandan, MR Kumar, VCS Rao… - Measurement, 2021‏ - Elsevier
Diabetes mellitus is a disease commonly called Diabetes. Diabetes is among the most
frequent diseases globally. This disease affects internationally with different ailments and …

RETRACTED ARTICLE: A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

S Akhbarifar, HHS Javadi, AM Rahmani… - Personal and Ubiquitous …, 2023‏ - Springer
Abstract Internet of Things (IoT) and smart medical devices have improved the healthcare
systems by enabling remote monitoring and screening of the patients' health conditions …

An AI-based intelligent system for healthcare analysis using Ridge-Adaline Stochastic Gradient Descent Classifier

N Deepa, B Prabadevi, PK Maddikunta… - The Journal of …, 2021‏ - Springer
Recent technological advancements in information and communication technologies
introduced smart ways of handling various aspects of life. Smart devices and applications …

The impact of data pre-processing techniques and dimensionality reduction on the accuracy of machine learning

HS Obaid, SA Dheyab, SS Sabry - 2019 9th annual information …, 2019‏ - ieeexplore.ieee.org
Data pre-processing is considered as the core stage in machine learning and data mining.
Normalization, discretization, and dimensionality reduction are well-known techniques in …

A novel enhanced hybrid clinical decision support system for accurate breast cancer prediction

LK Singh, M Khanna - Measurement, 2023‏ - Elsevier
Feature selection is one of the crucial data preprocessing techniques for improving the
performance of machine learning (ML) models. Recently, metaheuristic feature selection …

FBSED based automatic diagnosis of COVID-19 using X-ray and CT images

PK Chaudhary, RB Pachori - Computers in Biology and Medicine, 2021‏ - Elsevier
This work introduces the Fourier-Bessel series expansion-based decomposition (FBSED)
method, which is an implementation of the wavelet packet decomposition approach in the …

Study of data-driven thermal sensation prediction model as a function of local body skin temperatures in a built environment

JH Choi, D Yeom - Building and Environment, 2017‏ - Elsevier
Current thermal/sensation models primarily rely on predefined formulas or empirically
defined recommendations, but fail to consider each individual's physiological characteristics …