Dataset Information

Modelling disease risk for amyloid A (AA) amyloidosis in non-human primates using machine learning.

ABSTRACT: Objective: Amyloid A (AA) amyloidosis is found in humans and non-human primates, but quantifying disease risk prior to clinical symptoms is challenging. We applied machine learning to identify the best predictors of amyloidosis in rhesus macaques from available clinical and pathology records. To explore potential biomarkers, we also assessed whether changes in circulating serum amyloid A (SAA) or lipoprotein profiles accompany the disease. Methods: We conducted a retrospective study using 86 cases and 163 controls matched for age and sex. We performed data reduction on 62 clinical, pathological and demographic variables, and applied multivariate modelling and model selection with cross-validation. To test the performance of our final model, we applied it to a replication cohort of 2,775 macaques. Results: The strongest predictors of disease were colitis, gastrointestinal adenocarcinoma, endometriosis, arthritis, trauma, diarrhoea and number of pregnancies. Sensitivity and specificity of the risk model were predicted to be 82%, and were assessed at 79 and 72%, respectively. Total, low density lipoprotein and high density lipoprotein cholesterol levels were significantly lower, and SAA levels and triglyceride-to-HDL ratios were significantly higher in cases versus controls. Conclusion: Machine learning is a powerful approach to identifying macaques at risk of AA amyloidosis, which is accompanied by increased circulating SAA and altered lipoprotein profiles.

SUBMITTER: Leung ET

PROVIDER: S-EPMC6667354 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Modelling disease risk for amyloid A (AA) amyloidosis in non-human primates using machine learning.

Leung Eric T ET Raboin Michael J MJ McKelvey Jessica J Graham Adam A Lewis Anne A Prongay Kamm K Cohen Aaron M AM Vinson Amanda A

Amyloid : the international journal of experimental and clinical investigation : the official journal of the International Society of Amyloidosis 20190618 3

<b>Objective:</b> Amyloid A (AA) amyloidosis is found in humans and non-human primates, but quantifying disease risk prior to clinical symptoms is challenging. We applied machine learning to identify the best predictors of amyloidosis in rhesus macaques from available clinical and pathology records. To explore potential biomarkers, we also assessed whether changes in circulating serum amyloid A (SAA) or lipoprotein profiles accompany the disease. <b>Methods:</b> We conducted a retrospective stud ...[more]

PMID: 31210531

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Project description:Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is projected that this will reach nearly 10% by 2045. The major challenge is that when machine learning-based classifiers are applied to such data sets for risk stratification, leads to lower performance. Thus, our objective is to develop an optimized and robust machine learning (ML) system under the assumption that missing values or outliers if replaced by a median configuration will yield higher risk stratification accuracy. This ML-based risk stratification is designed, optimized and evaluated, where: (i) the features are extracted and optimized from the six feature selection techniques (random forest, logistic regression, mutual information, principal component analysis, analysis of variance, and Fisher discriminant ratio) and combined with ten different types of classifiers (linear discriminant analysis, quadratic discriminant analysis, naïve Bayes, Gaussian process classification, support vector machine, artificial neural network, Adaboost, logistic regression, decision tree, and random forest) under the hypothesis that both missing values and outliers when replaced by computed medians will improve the risk stratification accuracy. Pima Indian diabetic dataset (768 patients: 268 diabetic and 500 controls) was used. Our results demonstrate that on replacing the missing values and outliers by group median and median values, respectively and further using the combination of random forest feature selection and random forest classification technique yields an accuracy, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve as: 92.26%, 95.96%, 79.72%, 91.14%, 91.20%, and 0.93, respectively. This is an improvement of 10% over previously developed techniques published in literature. The system was validated for its stability and reliability. RF-based model showed the best performance when outliers are replaced by median values.

Dataset Information

Modelling disease risk for amyloid A (AA) amyloidosis in non-human primates using machine learning.

Publications

Modelling disease risk for amyloid A (AA) amyloidosis in non-human primates using machine learning.

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