Evaluation of machine learning algorithms for improved risk assessment for Down's syndrome

Koivu, Aki; Korpimäki, Teemu; Kivelä, Petri; Pahikkala, Tapio; Sairanen, Mikko

doi:10.1016/j.compbiomed.2018.05.004

Cited by 27 publications

(24 citation statements)

References 22 publications

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“…Beside various earlier studies [7,15,27,30], this investigation further establishes the role of machine learning models as tools that can generate risk prediction models that show improved clinical prediction power over a multivariate logistic regression model, which was used as a control and represents the current standard method. Furthermore, the ensemble models across three algorithms further improved the performance with late stillbirth.…”

Section: Discussionmentioning

confidence: 74%

“…For ANN, the first model was a Leaky ReLU-based deep two-layer feed-forward neural network that we have previously shown to perform well in the risk prediction task of Down's syndrome [15]. The second was a deep feed-forward self-normalizing neural network based on the scaled exponential linear units (SELU) activation function, which has been demonstrated to achieve superior performance to other feed-forward neural networks [14].…”

Section: Risk Prediction Modellingmentioning

confidence: 99%

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Predicting risk of stillbirth and preterm pregnancies with machine learning

Koivu

Sairanen

2020

Health Inf Sci Syst

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Modelling the risk of abnormal pregnancy-related outcomes such as stillbirth and preterm birth have been proposed in the past. Commonly they utilize maternal demographic and medical history information as predictors, and they are based on conventional statistical modelling techniques. In this study, we utilize state-of-the-art machine learning methods in the task of predicting early stillbirth, late stillbirth and preterm birth pregnancies. The aim of this experimentation is to discover novel risk models that could be utilized in a clinical setting. A CDC data set of almost sixteen million observations was used conduct feature selection, parameter optimization and verification of proposed models. An additional NYC data set was used for external validation. Algorithms such as logistic regression, artificial neural network and gradient boosting decision tree were used to construct individual classifiers. Ensemble learning strategies of these classifiers were also experimented with. The best performing machine learning models achieved 0.76 AUC for early stillbirth, 0.63 for late stillbirth and 0.64 for preterm birth while using a external NYC test data. The repeatable performance of our models demonstrates robustness that is required in this context. Our proposed novel models provide a solid foundation for risk prediction and could be further improved with the addition of biochemical and/or biophysical markers.

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Section: Discussionmentioning

confidence: 74%

Section: Risk Prediction Modellingmentioning

confidence: 99%

Predicting risk of stillbirth and preterm pregnancies with machine learning

Koivu

Sairanen

2020

Health Inf Sci Syst

Self Cite

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“…Regarding the targeted patient, two different contexts were focused on, i.e., postnatal or antenatal diagnosis. Most of the articles (61/68) focused on diagnosis after birth, while 7 studies consisted of prenatal screening for fetal syndromes [12], diseases with chromosomal abnormalities [13], aneuploidies [14][15][16] and trisomy [17,18] based on noninvasive markers (demographics, sonographic markers, maternal blood). The two contexts are referred to in the following sections as "the post-natal studies" and "the prenatal studies".…”

Section: Publication Targetmentioning

confidence: 99%

Diagnosis support systems for rare diseases: a scoping review

Faviez

Chen

Garcelon

et al. 2020

Orphanet J Rare Dis

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Introduction: Rare diseases affect approximately 350 million people worldwide. Delayed diagnosis is frequent due to lack of knowledge of most clinicians and a small number of expert centers. Consequently, computerized diagnosis support systems have been developed to address these issues, with many relying on rare disease expertise and taking advantage of the increasing volume of generated and accessible health-related data. Our objective is to perform a review of all initiatives aiming to support the diagnosis of rare diseases. Methods: A scoping review was conducted based on methods proposed by Arksey and O'Malley. A charting form for relevant study analysis was developed and used to categorize data. Results: Sixty-eight studies were retained at the end of the charting process. Diagnosis targets varied from 1 rare disease to all rare diseases. Material used for diagnosis support consisted mostly of phenotype concepts, images or fluids. Fifty-seven percent of the studies used expert knowledge. Two-thirds of the studies relied on machine learning algorithms, and one-third used simple similarities. Manual algorithms were encountered as well. Most of the studies presented satisfying performance of evaluation by comparison with references or with external validation. Fourteen studies provided online tools, most of which aimed to support the diagnosis of all rare diseases by considering queries based on phenotype concepts. Conclusion: Numerous solutions relying on different materials and use of various methodologies are emerging with satisfying preliminary results. However, the variability of approaches and evaluation processes complicates the comparison of results. Efforts should be made to adequately validate these tools and guarantee reproducibility and explicability.

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“…It has the parameter k which is used to split the datasets into groups. In our datasets, we have used k=10-fold cross-validations which is most reliable to evaluate the models and most of the previous study reported to use 10fold cross-validation [38]. The procedure of the k-Fold cross validation is to split the datasets into training and testing datasets into k groups.…”

Section: Related Workmentioning

confidence: 99%

A Predictive Performance Analysis of Vitamin D Deficiency Severity Using Machine Learning Methods

2020

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Vitamin D Deficiency (VDD) is one of the most significant global health problem and there is a strong demand for the prediction of its severity using non-invasive methods. The primary data containing serum Vitamin D levels were collected from a total of 3044 college students between 18-21 years of age. The independent parameters like age, sex, weight, height, body mass index (BMI), waist circumference, body fat, bone mass, exercise, sunlight exposure, and milk consumption were used for prediction of VDD. The study aims to compare and evaluate different machine learning models in the prediction of severity in VDD. The objectives of our approach are to apply various powerful machine learning algorithms in prediction and evaluate the results with different performance measures like Precision, Recall, F1-measure, Accuracy, and Area under the curve of receiver operating characteristic (ROC). The McNemar's test was conducted to validate the empirical results which is a statistical test. The final objective is to identify the best machine learning classifier in the prediction of the severity of VDD. The most popular and powerful machine learning classifiers like K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), AdaBoost (AB), Bagging Classifier (BC), ExtraTrees (ET), Stochastic Gradient Descent (SGD), Gradient Boosting (GB), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) were implemented to predict the severity of VDD. The final experimentation results showed that the Random Forest Classifier achieves better accuracy of 96 % and outperforms well on training and testing Vitamin D dataset. The McNemar's statistical test results support that the RF classifier outperforms than the other classifiers.

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Evaluation of machine learning algorithms for improved risk assessment for Down's syndrome

Cited by 27 publications

References 22 publications

Predicting risk of stillbirth and preterm pregnancies with machine learning

Predicting risk of stillbirth and preterm pregnancies with machine learning

Diagnosis support systems for rare diseases: a scoping review

A Predictive Performance Analysis of Vitamin D Deficiency Severity Using Machine Learning Methods

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