Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
Volume-3 | Issue-3
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique
Volume-3 | Issue-3
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
QoS-aware Virtual Machine (VM) for Optimal Resource Utilization and Energy Conservation
Volume-3 | Issue-3
Probabilistic Neural Network based Managing Algorithm for Building Automation System
Volume-3 | Issue-4
Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach
Volume-3 | Issue-1
Multi-scale CNN Approach for Accurate Detection of Underwater Static Fish Image
Volume-3 | Issue-3
Real Time Anomaly Detection Techniques Using PySpark Frame Work
Volume-2 | Issue-1
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
Volume-3 | Issue-4
Frontiers of AI beyond 2030: Novel Perspectives
Volume-4 | Issue-4
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
Volume-3 | Issue-4
ARTIFICIAL INTELLIGENCE APPLICATION IN SMART WAREHOUSING ENVIRONMENT FOR AUTOMATED LOGISTICS
Volume-1 | Issue-2
Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert
Volume-3 | Issue-2
Volume - 5 | Issue - 2 | june 2023
Published
28 June, 2023
Now a day due to hectic schedules and sedentary lifestyle people do not follow the proper diet. Poor diet may lead to diabetes, and which could result in various health issues such as heart attacks, strokes, renal failure, nerve damage, etc. When diabetes is accurately detected in its early stage , it can be effectively treated. By using Machine Learning methods, the problem can be easily detected and a solution could bearrived. Early diabetes detection and prediction can be greatly improved with machine learning (ML) approaches. When it is detected in an early stage, it can be resolved quickly. The objective of this research is to provide prediction using various supervised machine learning methods. Seven algorithms are compared with each other to figure out which is the best. The algorithms are Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Naïve Bayes, Gradient Boosting. The evaluation results stated that Logistic Regression is more accurate than other algorithms for the given data set with an accuracy of 82%. After selecting the ML model which is more accurate. A User Interface where users can enter the new data and get results was developed and the results to the user were forwarded through WhatsApp along with some suggestions and precautions.
KeywordsLogistic Regression Random Forest Decision Tree K-Nearest Neighbor Support Vector Machine Naïve Bayes Gradient Boosting Machine Learning (ML) Diabetes
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