ArticleScientific reports2021
Development of machine learning model for diagnostic disease prediction based on laboratory tests.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed.
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- Artificial intelligence for screening drug resistance in tuberculosis.The Indian journal of medical research · 2025Article
- Context matters in machine learning based disease prediction with insights from diverse clinical and symptom data.Scientific reports · 2025Article
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- Improving Type 2 Diabetes Prediction: Comparative Evaluation of Machine Learning Classifiers Using Balanced Data from the AWI-Gen Cohort.Research square · 2025Article
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- Predicting risk of early-onset sepsis in low-resource neonatal units using routine healthcare data: development and evaluation of multivariable statistical and machine learning models.BMJ paediatrics open · 2025Article
- Machine learning prediction and explanation of high intraoperative blood pressure variability for noncardiac surgery using preoperative factors.BMC cardiovascular disorders · 2025Observational
- Identification of age-specific risk factors for hyperuricemia: a machine learning-driven stratified analysis in health examination cohorts.BMC medical informatics and decision making · 2025Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- Impact of nutrition-related laboratory tests on mortality of patients who are critically ill using artificial intelligence: A focus on trace elements, vitamins, and cholesterol.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2025Article
- Multi-view contrastive learning and symptom extraction insights for medical report generation.Scientific reports · 2025Article
- Application of machine learning techniques in GlaucomAI system for glaucoma diagnosis and collaborative research support.Scientific reports · 2025Article
7 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ML models for disease prediction using laboratory test results. 86 attributes (laboratory tests) were selected from datasets based on value counts, clinical importance-related features, and missing values. We collected sample datasets on 5145 cases, including 326,686 laboratory test results. We investigated a total of 39 specific diseases based on the International Classification of Diseases, 10th revision (ICD-10) codes. These datasets were used to construct light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) ML models and a DNN model using TensorFlow. The optimized ensemble model achieved an F1-score of 81% and prediction accuracy of 92% for the five most common diseases. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Our new ML model achieved high efficiency of disease prediction through classification of diseases. This study will be useful in the prediction and diagnosis of diseases.
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