Regression Imputation and Optimized Gaussian Naïve Bayes Algorithm for an Enhanced Diabetes Mellitus Prediction Model
Abstract Diabetes mellitus (DM) is a category of metabolic disorders caused by high blood sugar. The DM affects human metabolism, and this disease causes many complications like Heart disease, Neuropathy, Diabetic retinopathy, kidney problems, skin disorder and slow healing. It is therefore essential to predict the presence of DM using an automated diabetes diagnosis system, which can be implemented using machine learning algorithms. A variety of automated diabetes prediction systems have been proposed in previous studies. Even so, the low prediction accuracy of DM prediction systems is a major issue. This proposed work developed a diabetes mellitus prediction system to improve the diabetes mellitus prediction accuracy using Optimized Gaussian Naive Bayes algorithm. This proposed model using the Pima Indians diabetes dataset as an input to build the DM predictive model. The missing values of an input dataset are imputed using regression imputation method. The sequential backward feature elimination method is used in this proposed model for selecting the relevant risk factors of diabetes disease. The proposed machine learning classifier named Optimized Gaussian Naïve Bayes (OGNB) is applied to the selected risk factors to create an enhanced Diabetes diagnostic system which predicts Diabetes in an individual. The performance analysis of this prediction architecture shows that, over other traditional machine learning classifiers, the Optimized Gaussian Naïve Bayes achieves an 81.85% classifier accuracy. This proposed DM prediction system is effective as compared to other diabetes prediction systems found in the literature. According to our experimental study, the OGNB based diabetes mellitus prediction system is more appropriate for DM disease prediction.
Main Authors: | Mohideen,Dhilsath Fathima Mohammed, Raj,Justin Samuel Savari, Raj,Raja Soosaimarian Peter |
---|---|
Format: | Digital revista |
Language: | English |
Published: |
Instituto de Tecnologia do Paraná - Tecpar
2021
|
Online Access: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132021000100624 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Hybrid Feature Ranking Algorithm for Assisted Reproductive Technology Outcome Prediction
by: Kothandaraman,Ranjini, et al.
Published: (2022) -
Imputed Welfare Estimates in Regression Analysis
by: Elbers, Chris, et al.
Published: (2004-04) -
An Experimental Analysis of Optimal Hybrid Word Embedding Methods for Text Classification Using a Movie Review Dataset
by: Alagarsamy,Sandhya, et al.
Published: (2022) -
Multi-task Gaussian process for imputing missing data in multi-trait and multi-environment trials
by: Hori, T., et al.
Published: (2021-12-03T07:56:44Z) -
Decision Tree Based Salp Swarm Optimization for Multi Medical Data Classification with Feature Reduction Technique
by: Sarala,Sakunthala Prabha Kadaksham, et al.
Published: (2021)