Predicting Multiple Diseases Using Machine Learning: A Symptom-Based Model for Medical Diagnosis

The new innovations of ML and DL has improved and delivering better results in many areas, with healthcare being one of them. The paper discussed in this work focuses on the use of the machine learning methods to predict multiple diseases given 132 symptoms. In this study, 42 diseases were considered as a target granule, and there are numerous training and testing samples. The proposed approach should be useful to physicians by giving a reliable method of disease prediction that can meet the objective of improving physicians' decision making and patients' care.

Authors

Institutions

Publication Details

Published
2024-11-07
DOI
https://doi.org/10.1109/icisct64202.2024.10956787
Citations
4
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
2.83
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predicting Multiple Diseases Using Machine Learning: A Symptom-Based Model for Medical Diagnosis

Khakimjon Zaynidinov, Sonal Pathak, Bakhtiyor Makhkamov, Ibrohimbek Yusupov et al.
4 citations
Artificial Intelligence in Healthcare
2.83
article

Predicting Multiple Diseases Using Machine Learning: A Symptom-Based Model for Medical Diagnosis

Khakimjon Zaynidinov, Sonal Pathak, Bakhtiyor Makhkamov, Ibrohimbek Yusupov, Temurbek Kuchkorov, Danish Ather
article en
4 citations

Abstract

The new innovations of ML and DL has improved and delivering better results in many areas, with healthcare being one of them. The paper discussed in this work focuses on the use of the machine learning methods to predict multiple diseases given 132 symptoms. In this study, 42 diseases were considered as a target granule, and there are numerous training and testing samples. The proposed approach should be useful to physicians by giving a reliable method of disease prediction that can meet the objective of improving physicians' decision making and patients' care.

Tashkent University of Information Technology (UZ)
Openalex Percentile: Top 7%
Artificial Intelligence in Healthcare
2.83
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Predicting Multiple Diseases Using Machine Learning: A Symptom-Based Model for Medical Diagnosis — Khakimjon Zaynidinov, Sonal Pathak, et al. · (2024) | TGRS Research Map | TGRS