A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3
A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2
A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3
Study of Security Mechanisms to Create a Secure Cloud in a Virtual Environment with the Support of Cloud Service Providers
Volume-2 | Issue-3
Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2
Fault Diagnosis in Hybrid Renewable Energy Sources with Machine Learning Approach
Volume-3 | Issue-3
Secure and Optimized Cloud-Based Cyber-Physical Systems with Memory-Aware Scheduling Scheme
Volume-2 | Issue-3
Stochastic Geometry and Performance Analysis of Large Scale Wireless Networks
Volume-3 | Issue-3
Computer Vision on IOT Based Patient Preference Management System
Volume-2 | Issue-2
Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4
A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3
Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4
A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3
Speedy Detection Module for Abandoned Belongings in Airport Using Improved Image Processing Technique
Volume-3 | Issue-4
Deployment of Artificial Intelligence with Bootstrapped Meta-Learning in Cyber Security
Volume-4 | Issue-3
Design an Early Detection and Classification for Diabetic Retinopathy by Deep Feature Extraction based Convolution Neural Network
Volume-3 | Issue-2
Design of an Intelligent Approach on Capsule Networks to Detect Forged Images
Volume-3 | Issue-3
Future Challenges of the Internet of Things in the Health Care Domain - An Overview
Volume-3 | Issue-4
Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2
A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2
Volume - 5 | Issue - 2 | june 2023
Published
27 June, 2023
Type 2 diabetes is a persistent disorder that affects millions of individuals globally. It is characterised by the excessive levels of glucose within the blood due to insulin resistance or the incapability to supply insulin. Early detection and prediction of type 2 diabetes can improve patient outcomes. K-Nearest Neighbor (KNN) is used in the present model to predict type 2 diabetes. The KNN set of rules is a simple but powerful machine learning set of rules used for categorization and regression. It's far a non-parametric approach that makes predictions based totally on the nearest k-neighbours in a dataset. KNN is widely used in healthcare and scientific studies to expect and classify sicknesses primarily based on the affected person’s data. The intention of this work is to predict the threat of growing type 2 diabetes using the KNN set of rules. Data has been collected from electronic medical records of patients diagnosed with type 2 diabetes and healthy individuals. The dataset consists of various patient attributes, such as age, gender, body mass index, blood pressure, cholesterol levels, and glucose levels. Information has also been collected about lifestyle habits, such as physical activity, smoking status, and alcohol consumption. Data have been pre-processed by removing missing values and outliers, and normalization of the data has been done to ensure that all features have the same scale. Splitting the dataset into training and test sets, with training sets using 80% of the data and test sets using 20% of the data is performed. KNN algorithm have been used to classify the patients into two groups: those at high risk of developing type 2 diabetes and those at low risk. The model's performance has been assessed using a variety of metrics, including accuracy, precision, recall, and F1-score.
KeywordsPrediction F1 Recall K Fold Confusion Matrix
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