Analysis and comparison for prediction of Diabetic Pregnant women using Innovative Principal Component Analysis algorithm over Support Vector Machine Algorithm with Improved Accuracy

Aim: The study's aim is to analyze and compare the accuracy, sensitivity, and precision of diabetic prediction among pregnant women using the innovative Principal Component Analysis algorithm and Support Vector Analysis. Materials and Methods: This study involves two groups: Principal Component...

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Published in:Cardiometry 2022-12 (25), p.942-948
Main Authors: Pokala, Venkata Sai Kumar, Kumar, Neelam Sanjeev
Format: Article
Language:English
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Accuracy
Algorithms
Diabetes
Pregnancy
Principal components analysis
Support vector machines
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description Aim: The study's aim is to analyze and compare the accuracy, sensitivity, and precision of diabetic prediction among pregnant women using the innovative Principal Component Analysis algorithm and Support Vector Analysis. Materials and Methods: This study involves two groups: Principal Component Analysis (N=20) algorithm and Support Vector Machine (N=20) with a sample size of 40 for each group. The sample size calculation uses a pre-test power of 80%, a threshold of 0.05, and a confidence interval of 95%. Results: Performance of algorithms are measured using accuracy, sensitivity, and precision. Principal Component Analysis algorithm results in mean accuracy of 79.43% significantly different with P=0.488(p>0.05), a sensitivity of 79.29% with P=0.096 (p
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subjects Accuracy
Algorithms
Diabetes
Pregnancy
Principal components analysis
Support vector machines
title Analysis and comparison for prediction of Diabetic Pregnant women using Innovative Principal Component Analysis algorithm over Support Vector Machine Algorithm with Improved Accuracy
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