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 - 6 | Issue - 1 | march 2024
Published
13 February, 2024
The proactive anticipation of disease occurrence stands as a pivotal facet within healthcare and medical research endeavors, dedicated to forecasting the probability of an individual manifesting a particular medical condition or ailment in the future. This fundamental pursuit integrates diverse data reservoirs, encompassing medical history, genetic profiles, lifestyle determinants, and emerging technological advancements, to construct predictive frameworks capable of furnishing early indications and insights pertaining to potential health vulnerabilities. The overarching aim of disease prediction resides in furnishing healthcare practitioners and individuals alike with the requisite knowledge and resources to undertake pre-emptive measures, render informed choices, and ultimately enhance holistic health and well-being. The Neural Network algorithm emerges as a dependable approach for disease prognostication, offering heightened precision and several advantages compared to the conventional methodologies, including its capacity to discern intricate features from images and its adaptability across diverse computing platforms. The proposed study offers a comprehensive review of disease prediction methods, comparing conventional approaches with machine learning interventions to provide swift and reliable results. Further review suggests a proposed model that utilizes the neural network algorithms to overcome the shortcomings of conventional methods.
KeywordsHealthcare Machine Learning Disease Prediction Artificial Intelligence
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