Application and Analysis of Machine Learning Algorithms on Pima and Early Diabetes Datasets for Diabetes Prediction

Abstract

Diabetes is a chronic condition that strike how your body burns food for energy. Much of the food you consume is converted by your body into sugar (glucose), which is then released into your bloodstream. Your pancreas releases insulin when your blood sugar levels rise. Over the years, several scholars have sought to create reliable diabetes prediction models. Due to a lack of adequate data sets and prediction techniques, this discipline still faces many unsolved research issues, which forces researchers to apply big data analytics and ML-based methodology. Four distinct machine learning algorithms are used in the study to analyze healthcare prediction analytics and solve the issues. In this investigation, the Pima and Early detection datasets were employed. We applied the Decision Tree, MLP, Naive Bayes, and Random Forest algorithms to these datasets and evaluated the accuracy and F-Measure. The goal of this research is to develop a system that could more precisely predict a patient's risk of developing diabetes.

Image based Seen Detection in Real Time Video Interpretation for Surveillance Systems using Support Vectors Machine

“…Then, algorithm evaluation is shown for detecting the realistic computer-generated video image, the effectiveness of SVM on different pictures is discussed here to detect the given image in the inputted video. The use of convolution neural network in image detection [17], and some other machine learning methods that are described in [18] are also used to compare the given data with the existing data. The paper [19], aims to propose an extensive evaluation of the precision in facial recognition by utilizing various characteristics captured by the and contrasting them with the features captured by regular cameras (specifically RGB images).…”