Performance Comparison of Naive Bayes (NB) and KNN Algorithm for the Prediction Accuracy of Diabetes under HBA1c and FBS Test
Keywords:
Accuracy, Artificial Intelligence (AI), Diabetes Mellitus (DM), Fasting Blood Sugar (FBS), Glycated Hemoglobin (HBA1C), K-Nearest Neighbors (KNN), Machine Learning (ML), Naïve Bayes (NB)Abstract
The progressive disease known as Diabetes Mellitus (DM) is brought on by the body's inability to use the insulin it generates efficiently. One way to diagnose is by applying Artificial Intelligence (AI). Developing technological devices capable of logic, gaining knowledge, and behaving in capacities that would typically need human expertise or require data that is significantly above anything humans can evaluate is the focus of the scientific discipline of artificial intelligence. A branch of Artificial Intelligence (AI) called Machine Learning (ML) enables computers to gain knowledge from their experiences and improve over time without specific programming. Algorithms are used in machine learning to evaluate data, identify patterns, and generate results. Two significant machine learning algorithms, NB and KNN, are used in this research to predict the accuracy of diabetes. To diagnose Diabetes Mellitus (DM) precisely, this study uses a dataset from The Smt. Manjira Devi Group of Institutions and Ayurvedic Medical College and Hospital in Uttarakhand, to determine the best classification of the Naïve Bayes (NB) and K-Nearest Neighbors (KNN) methods. The research presented here states that two machine learning algorithms, NB and KNN, have been used to predict diabetes. The two algorithms have been modified to test the HBA1C and FBS datasets. After investigation, it was determined that the KNN algorithm outperforms the NB method in terms of prediction accuracy.
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