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International Journal of Basic Science and Technology

A publication of the Faculty of Science, Federal University Otuoke, Bayelsa State

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Archive | ISSUE: , Volume: Apr-Jun-2024

An Improved Model for Feature Selection on Type 2 Diabetes Risk Prediction in Nigeria


Author:Moko, A.,Onuodu, F.E

published date:2024-Mar-03

FULL TEXT in - | page 123 - 133

Abstract

Diabetes mellitus (DM) is one of the world's fatal diseases, mostly in developed countries. It has become more predominant in developing countries such as Nigeria in recent years, posing more risks to individuals in the latter than the former. This study proposed a risk prediction model for Type 2 diabetes in Nigeria with feature selection adopting the qualitative and quantitative methodology. The Federal Medical Centre in Otuoke offered the dataset for this research used to develop the risk predictive machine learning models using python programming language in Collaboratory google based on logistic regression that uses dynamic regression technique to build predictors as a boolean combination of binary predictor variables, support vector machine a form of supervised learning technique widely used in classification and regression for medical diagnostic applications, gradient boosting builds a stadium additive model by executing a gradient descent in functional space, which is one of the most reliable and widely ensemble supervised learning methods, Decision Tree one of the most widely used data classification methods and Random Forest measures the quality of features by the impurity index, which means the average reduction from the division with the variable over all trees. The results attained revealed that in terms of accuracy and performance, the Gradient Boosting Algorithm predictive technique appears to be one of the optimally designed models, with 98%, followed by Decision Tree at 96% and Random Forest at 94% as their predictive accuracy score. The models can be incorporated into a digital system to help doctors better predict diabetes in patients and provide appropriate control measures. Sex, Age, Body Mass Index (BMI), Blood Pressure, Pulse rate, and Respiratory rate are the vital predictors in these models

Keywords: Risk prediction, Models, Feature selection, ,

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