International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 16 Issue 2 April-June 2025 Submit your research before last 3 days of June to publish your research paper in the issue of April-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 16 Issue 2
April-June 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

ASHWINI: Assistive Systems for Healthcare and Well-Being with Intelligent Neural Network Integration

Author(s) Shubhagaman Singh, Shiphali Chaurasia, Kritya Kumar, Dharmendra Chauhan, Dr. Krishna Kumar
Country India
Abstract The use of machine learning methods in healthcare has greatly increased the precision
and effectiveness of illness diagnosis and treatment planning in recent years. In addition to
predicting the probability of serious illnesses, including diabetes, heart disease, renal disease, and
breast cancer, this research suggests a comprehensive approach that also suggests suitable
medications based on the anticipated condition. For improved accuracy and optimization, the
system combines more sophisticated methods like Multi-Layer Perceptron Neural Networks
(MLPNN), Recurrent Neural Networks (RNN), and Particle Swarm Optimization (PSO) with
more conventional machine learning models like K-Nearest Neighbors (KNN), Support Vector
Machines (SVM), Random Forest, and Logistic Regression. Intelligent mapping of disease-drug
correlations is used to develop medicine recommendations, and publicly available medical datasets
are used for training and evaluation. By bridging the gap between disease identification and
therapy recommendation, the suggested design seeks to improve the intelligence and patient
centricity of the healthcare process. According to experimental data, the hybrid model shows
promise for practical clinical decision support systems by improving predictive performance and
boosting the dependability of suggested treatments.
Keywords Disease Prediction, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest, and Logistic Regression, Machine Learning, Artificial Intelligence (AI), Multi Layer Perceptron Neural Networks (MLPNN), Recurrent Neural Networks (RNN), and Particle Swarm Optimization (PSO), Medicine Recommendation System, Healthcare System
Field Medical / Pharmacy
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-05-19
DOI https://doi.org/10.71097/IJSAT.v16.i2.5324
Short DOI https://doi.org/g9mn9b
View / Download PDF File

Share this


CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJSAT DOI prefix is
10.71097/IJSAT

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 4Cover Page Vol 15 Isu 3Cover Page Vol 15 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJSAT
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijsat.org