Classification of diabetic patients records using Naïve Bayes classifier

Thulasi, K S; Ninu, E S; Shiva, Kumar K. M

doi:10.1109/rteict.2017.8256787

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…The naive Bayes classifier is regarded as every characteristic separately to compute the characteristic goods, which gives the possibility of a particular classification output. After that, it applies Laplace modification for avoiding great encounters of zero possibilities [9].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Learning method for improving the Healthcare IoT System

Ramakrishnan¹,

Chirputkar²

2023

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Wireless Body Area Network (WBAN) is several wearable sensor nodes with unstable sensing, storage, computation, and communication capabilities. Heart infection is an essential origin of death internationally, and early recognition is vital in avoiding the development of the infection. This article presents an Ensemble Learning (EL) method for improving the Healthcare Internet of Things (IoT) System. This approach aims to forecast the risk of heart infection. This method involves dividing the dataset into subgroups at random using a mean-based split. The data is then divided into multiple groups using a classification and regression tree. A weighted aging classifier ensemble is used to create an ensemble from the many trees used for classification and regression. It makes sure an optimal function is reached. The experimental outcomes on the datasets reached better classification accuracies that outperformed other machine learning algorithms like Artificial Neural Network, Naıve Bayes, and Support Vector Machine. The outcomes demonstrate that the risk of heart infection can be forecasted efficiently through ensemble learning.

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Section: Related Workmentioning

confidence: 99%

Ensemble Learning method for improving the Healthcare IoT System

Ramakrishnan¹,

Chirputkar²

2023

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“…The Naive Bayes Classifier (NBC) is based on the Bayesian theorem for constructing classifiers (Thulasi, Ninu, & Shiva, 2017). The main concept here is to infer, on the basis of known results, the probable occurrence of a certain reason.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Fusion Based on Cloud Computing Environment for Long-Term Care

Zhuang

Hsu

2020

International Journal of Healthcare Information Systems and Informatics

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Globally, aging is now a societal trend and challenge in many developed and developing countries. A key medical strategy that a fast-paced aging society must consider is the provision of quality long-term care (LTC) services. Even so, the lack of LTC caregivers is a persistent global problem. Herein, attention is called to the increasing need for identifying appropriate LTC caregivers and delivering client-specific LTC services to the elderly via emerging and integrative technologies. This paper argues for the use of an intelligent cloud computing long-term care platform (ICCLCP) that integrates statistical analysis, machine learning, and Semantic Web technologies into a cloud-computing environment to facilitate LTC services delivery. The Term frequency-inverse document frequency is a numerical statistic adopted to automatically assess the professionalism of each LTC caregiver's services. The machine learning method adopts naïve Bayes classifier to estimate the LTC services needed for the elderly. These two items of LTC information are integrated with the Semantic Web to provide an intelligent LTC framework. The deployed ICCLCP will then aid the elderly in the recommendation of LTC caregivers, thereby making the best use of available resources for LTC services.

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