ArticleFrontiers in public health2022
An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Leakage-Controlled and Survey-Weighted Machine Learning for Neonatal Mortality Risk Prediction Using NFHS-5 Data.Healthcare (Basel, Switzerland) · 2026Article
- Personalized multi-agent reinforcement learning framework for adaptive chronic disease therapy management.Scientific reports · 2026Article
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Health Promotion and Disease Reduction: Rapid Review.Journal of medical Internet research · 2025Review
- Application of machine learning for early detection of chronic diseases in Africa.Journal of public health research · 2025Article
- Integrating Social Determinants of Health and Established Risk Factors to Predict Cardiovascular Disease Risk Among Healthy Older Adults.Journal of the American Geriatrics Society · 2025Article
- Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models.Pain and therapy · 2025Article
- Development of risk models for early detection and prediction of chronic kidney disease in clinical settings.Scientific reports · 2024Article
- Comorbidity progression analysis: patient stratification and comorbidity prediction using temporal comorbidity network.Health information science and systems · 2024Article
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- Health Equity and Ethical Considerations in Using Artificial Intelligence in Public Health and Medicine.Preventing chronic disease · 2024Article
- Patient and Caregiver Perceptions of an Interface Design to Communicate Artificial Intelligence-Based Prognosis for Patients With Advanced Solid Tumors.JCO clinical cancer informatics · 2024Article
- Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics.PeerJ. Computer science · 2024Article
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- A review on the use of machine learning techniques in monkeypox disease prediction.Science in One Health · 2023Review
- Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models.Frontiers in public health · 2023Article
- Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population.Advances in geriatric medicine and research · 2023Article
- A robust deep learning approach for tomato plant leaf disease localization and classification.Scientific reports · 2022Article
- Gene signature of mScientific reports · 2022Article
- Transfer learning-based modified inception model for the diagnosis of Alzheimer's disease.Frontiers in computational neuroscience · 2022Article
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Authors and funding
7 authors.
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Abstract
Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems.
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