Deep learning based big medical data analytic model for diabetes complication prediction

Introduction Sudden unexpected death in infancy (SUDI) represents the commonest presentation of postneonatal death. We explored whether machine learning could be used to derive data driven insights for prediction of infant autopsy outcome. Methods A paediatric autopsy database containing >7,000 cases, with >300 variables, was analysed by examination stage and autopsy outcome classified as ‘explained (medical cause of death identified)’ or ‘unexplained’. Decision tree, random forest, and gradient boosting models were iteratively trained and evaluated. Results Data from 3,100 infant and young child (<2 years) autopsies were included. Naïve decision tree using external examination data had performance of 68% for predicting an explained death. Core data items were identified using model feature importance. The most effective model was XG Boost, with overall predictive performance of 80%, demonstrating age at death, and cardiovascular and respiratory histological findings as the most important variables associated with determining medical cause of death. Conclusion This study demonstrates feasibility of using machine-learning to evaluate component importance of complex medical procedures (paediatric autopsy) and highlights value of collecting routine clinical data according to defined standards. This approach can be applied to a range of clinical and operational healthcare scenarios

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“…The main disadvantage of the technique is that using a single tree a model will suffer from low variance and high bias. 19…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Approaches to Determine Feature Importance for Predicting Infant Autopsy Outcome

et al. 2021

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“…The prediction performance of the APGWO-MLP model was superior to the other models used for comparison. K. Vidhya et al [ 21 ], 2020, proposed diabetes prediction based on deep belief network (DBN). They preprocessed the dataset using traditional preprocessing techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics

Sahid,

Babar,

Uddin

2024

PLoS ONE

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Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose, commonly referred to as blood sugar. This condition can have detrimental effects on the heart, blood vessels, eyes, kidneys, and nerves as time passes. It is a chronic ailment that arises when the body fails to produce enough insulin or is unable to effectively use the insulin it produces. When diabetes is not properly managed, it often leads to hyperglycemia, a condition characterized by elevated blood sugar levels or impaired glucose tolerance. This can result in significant harm to various body systems, including the nerves and blood vessels. In this paper, we propose a multiclass diabetes mellitus detection and classification approach using an extremely imbalanced Laboratory of Medical City Hospital data dynamics. We also formulate a new dataset that is moderately imbalanced based on the Laboratory of Medical City Hospital data dynamics. To correctly identify the multiclass diabetes mellitus, we employ three machine learning classifiers namely support vector machine, logistic regression, and k-nearest neighbor. We also focus on dimensionality reduction (feature selection—filter, wrapper, and embedded method) to prune the unnecessary features and to scale up the classification performance. To optimize the classification performance of classifiers, we tune the model by hyperparameter optimization with 10-fold grid search cross-validation. In the case of the original extremely imbalanced dataset with 70:30 partition and support vector machine classifier, we achieved maximum accuracy of 0.964, precision of 0.968, recall of 0.964, F1-score of 0.962, Cohen kappa of 0.835, and AUC of 0.99 by using top 4 feature according to filter method. By using the top 9 features according to wrapper-based sequential feature selection, the k-nearest neighbor provides an accuracy of 0.935 and 1.0 for the other performance metrics. For our created moderately imbalanced dataset with an 80:20 partition, the SVM classifier achieves a maximum accuracy of 0.938, and 1.0 for other performance metrics. For the multiclass diabetes mellitus detection and classification, our experiments outperformed conducted research based on the Laboratory of Medical City Hospital data dynamics.

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“…Prediction of Diabetes Using Bayesian Network (Kadhm et al, 2018) Technique, Algorithms, Machine learning, Classification. Type II Diabetes Analysis using Naïve Bayesian Classification Algorithm (Vidhya y Shanmugalakshmi, 2020a) Type II Diabetes, Machine learning, Algorithms, Prediction.…”

Section: Classification Algorithmsmentioning

confidence: 99%

Estudio de algoritmos de inteligencia artificial más utilizados para el diagnóstico de diabetes mellitus tipo 2

Cuenca Andrade,

Guamán-Quinche,

Álvarez-Pineda

et al. 2023

CEDAMAZ

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La diabetes es la segunda causa de muerte a nivel mundial sobre todo en países de bajos recursos. En el Ecuador uno de cada diez personas es diagnosticado con diabetes mellitus tipo 2, esto es debido a factores de riesgo como: antecedentes familiares con diabetes, medicación, sedentarismo o mala alimentación. Por lo tanto, es indispensable hacer una Revisión Sistemática de Literatura sobre el estado del uso de técnicas o algorítmos de Inteligencia Artificial para el diagnóstico de diabetes mellitus tipo 2; con este fin de responder la pregunta: ¿Cuáles son las técnicas de inteligencia artificial aplicadas al diagnóstico de diabetes mellitus tipo 2?

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Deep learning based big medical data analytic model for diabetes complication prediction

Cited by 23 publications

References 23 publications

Machine Learning Approaches to Determine Feature Importance for Predicting Infant Autopsy Outcome

Machine Learning Approaches to Determine Feature Importance for Predicting Infant Autopsy Outcome

Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics

Estudio de algoritmos de inteligencia artificial más utilizados para el diagnóstico de diabetes mellitus tipo 2

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