ArticleJournal of healthcare engineering2021
Efficient Automated Disease Diagnosis Using Machine Learning Models.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Cytokines in cerebrospinal fluid combined with machine learning improve the diagnostic accuracy and predict the progression of neurosyphilis.Frontiers in immunology · 2026Article
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- A high precision and speed question answering system about the post-COVID-19.Scientific reports · 2025Article
- Immuno-transcriptomic analysis based on machine learning identifies immunity signature genes of chronic rhinosinusitis with nasal polyps.Scientific reports · 2025Article
- Artificial Intelligence in Biomedical Engineering and Its Influence on Healthcare Structure: Current and Future Prospects.Bioengineering (Basel, Switzerland) · 2025Review
- SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence.Scientific reports · 2025Article
- Peripheral myelin protein 2 is underexpressed in early-onset colorectal cancer and inhibits metastasis.Frontiers in molecular biosciences · 2025Article
- Characterizing Duodenal Immune Microenvironment in Functional Dyspepsia: An AutoML-Driven Diagnostic Framework.Journal of inflammation research · 2025Article
- Uncovering key biomarkers, potential therapeutic targets and development of deep learning model in heart failure.PloS one · 2025Article
- Toward real-world deployment of machine learning for health care: External validation, continual monitoring, and randomized clinical trials.Health care science · 2024Article
- SecureVision: Advanced Cybersecurity Deepfake Detection with Big Data Analytics.Sensors (Basel, Switzerland) · 2024Article
- Scientific Production Dynamics in mHealth for Diabetes: Scientometric Analysis.JMIR diabetes · 2024Article
- Identification and validation of potential common biomarkers for papillary thyroid carcinoma and Hashimoto's thyroiditis through bioinformatics analysis and machine learning.Scientific reports · 2024Article
- Exploring the Roles of Key Mediators IKBKE and HSPA1A in Alzheimer's Disease and Hepatocellular Carcinoma through Bioinformatics Analysis.International journal of molecular sciences · 2024Article
- Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia.BMC medical informatics and decision making · 2024Article
- Identification and Analysis of PANoptosis-Related Genes in Sepsis-Induced Lung Injury by Bioinformatics and Experimental Verification.Journal of inflammation research · 2024Article
- Using Bioinformatics and Machine Learning to Predict the Genetic Characteristics of Ferroptosis-Cuproptosis-Related Genes Associated with Sleep Deprivation.Nature and science of sleep · 2024Article
- Elucidating macrophage scavenger receptor 1's mechanistic contribution as a shared molecular mediator in obesity and thyroid cancer pathogenesis via bioinformatics analysis.Frontiers in genetics · 2024Article
- Identification of immune-related genes for the diagnosis of ischemic heart failure based on bioinformatics.iScience · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, many researchers have designed various automated diagnosis models using various supervised learning models. An early diagnosis of disease may control the death rate due to these diseases. In this paper, an efficient automated disease diagnosis model is designed using the machine learning models. In this paper, we have selected three critical diseases such as coronavirus, heart disease, and diabetes. In the proposed model, the data are entered into an android app, the analysis is then performed in a real-time database using a pretrained machine learning model which was trained on the same dataset and deployed in firebase, and finally, the disease detection result is shown in the android app. Logistic regression is used to carry out computation for prediction. Early detection can help in identifying the risk of coronavirus, heart disease, and diabetes. Comparative analysis indicates that the proposed model can help doctors to give timely medications for treatment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.