ArticleJournal of healthcare engineering2022
Identification and Prediction of Chronic Diseases Using Machine Learning Approach.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 131 citations in OpenAlex.
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- Prevalence, risk-factors and early prediction of impaired kidney function among indigenous adults in Odisha, India: A secondary analysis.The Indian journal of medical research · 2026Article
- The Danish Chronic Disease Cohort: using digital footprints to identify chronic disease patterns.European journal of epidemiology · 2026Article
- Machine learning used to study risk factors for chronic diseases: A scoping review.Canadian journal of public health = Revue canadienne de sante publique · 2026Article
- Chronic liver disease detection using deep convolutional neural networks with MRI data: a deep learning approach.Polish journal of radiology · 2026Article
- IoMT driven Alzheimer's prediction model empowered with transfer learning and explainable AI approach in healthcare 5.0.Scientific reports · 2025Article
- Protocol for an Integrative Meta-Analysis of the Application of Machine Learning Algorithms in the Prediction of Chronic Disease Risks and Outcomes.Chronic diseases and translational medicine · 2025Article
- Precision management in chronic disease: An AI empowered perspective on medicine-engineering crossover.iScience · 2025Review
- Integrating feature importance techniques and causal inference to enhance early detection of heart disease.PloS one · 2025Article
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- Multimodal Identification of Molecular Factors Linked to Severe Diabetic Foot Ulcers Using Artificial Intelligence.International journal of molecular sciences · 2024Article
- Leverage machine learning to identify key measures in hospital operations management: a retrospective study to explore feasibility and performance of four common algorithms.BMC medical informatics and decision making · 2024Article
- Integrating predictive modeling and causal inference for advancing medical science.Childhood kidney diseases · 2024Review
- Machine Learning-Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals.JMIRx med · 2024Article
- Applying machine learning in screening for Down Syndrome in both trimesters for diverse healthcare scenarios.Heliyon · 2024Article
- Opposing Effects of Nutritional Supply on Bone Health at Different Ages: Based on the National Health and Nutrition Examination Survey Database.Nutrients · 2024Article
- Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research.Diagnostics (Basel, Switzerland) · 2024Review
- Prediction of Major Adverse Cardiac Events After Transcatheter Aortic Valve Implantation: A Machine Learning Approach with GRACE Score.Sisli Etfal Hastanesi tip bulteni · 2024Article
- Hyperparameter optimization for cardiovascular disease data-driven prognostic system.Visual computing for industry, biomedicine, and art · 2023Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, humans face various diseases due to the current environmental condition and their living habits. The identification and prediction of such diseases at their earlier stages are much important, so as to prevent the extremity of it. It is difficult for doctors to manually identify the diseases accurately most of the time. The goal of this paper is to identify and predict the patients with more common chronic illnesses. This could be achieved by using a cutting-edge machine learning technique to ensure that this categorization reliably identifies persons with chronic diseases. The prediction of diseases is also a challenging task. Hence, data mining plays a critical role in disease prediction. The proposed system offers a broad disease prognosis based on patient's symptoms by using the machine learning algorithms such as convolutional neural network (CNN) for automatic feature extraction and disease prediction and K-nearest neighbor (KNN) for distance calculation to find the exact match in the data set and the final disease prediction outcome. A collection of disease symptoms has been performed for the preparation of the data set along with the person's living habits, and details related to doctor consultations are taken into account in this general disease prediction. Finally, a comparative study of the proposed system with various algorithms such as Naïve Bayes, decision tree, and logistic regression has been demonstrated in this paper.
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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.