Systematic Review on Missing Data Imputation Techniques with Machine Learning Algorithms for Healthcare

Missing data is one of the most common issues encountered in data cleaning process especially when dealing with medical dataset. A real collected dataset is prone to be incomplete, inconsistent, noisy and redundant due to potential reasons such as human errors, instrumental failures, and adverse death. Therefore, to accurately deal with incomplete data, a sophisticated algorithm is proposed to impute those missing values. Many machine learning algorithms have been applied to impute missing data with plausible values. However, among all machine learning imputation algorithms, KNN algorithm has been widely adopted as an imputation for missing data due to its robustness and simplicity and it is also a promising method to outperform other machine learning methods. This paper provides a comprehensive review of different imputation techniques used to replace the missing data. The goal of the review paper is to bring specific attention to potential improvements to existing methods and provide readers with a better grasps of imputation technique trends

Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring

BACKGROUND AND OBJECTIVES: Intrauterine Growth Restriction (IUGR) is a fetal condition defined as the abnormal rate of fetal growth. The pathology is a documented cause of fetal and neonatal morbidity and mortality. In clinical practice, diagnosis is confirmed at birth and may only be suspected during pregnancy. Therefore, designing an accurate model for the early and prompt identification of pathology in the antepartum period is crucial in view of pregnancy management. METHODS: We tested the performance of 15 machine learning techniques in discriminating healthy versus IUGR fetuses. The various models were trained with a set of 12 physiology based heart rate features extracted from a single antepartum CardioTocographic (CTG) recording. The reason for the utilization of time, frequency, and nonlinear indices is based on their standalone documented ability to describe several physiological and pathological fetal conditions. RESULTS: We validated our approach on a database of 60 healthy and 60 IUGR fetuses. The machine learning methodology achieving the best performance was Random Forests. Specifically, we obtained a mean classification accuracy of 0.911 [0.860, 0.961 (0.95 confidence interval)] averaged over 10 test sets (10 Fold Cross Validation). Similar results were provided by Classification Trees, Logistic Regression, and Support Vector Machines. A features ranking procedure highlighted that nonlinear indices showed the highest capability to discriminate between the considered fetal conditions. Nevertheless, is the combination of features investigating CTG signal in different domains, that contributes to an increase in classification accuracy. CONCLUSIONS: We provided validation of an accurate artificially intelligence framework for the diagnosis of IUGR condition in the antepartum period. The employed physiology based heart rate features constitute an interpretable link between the machine learning results and the quantitative estimators of fetal wellbeing

IDENTIFICATION OF HYPOXIA-REGULATED MICRORNAs IN MATERNAL BLOOD AS EARLY PERIPHERAL BIOMARKERS FOR FETAL GROWTH RESTRICTION

Current tests available to diagnose fetal hypoxia in-utero lack sensitivity thus failing to identify many fetuses at risk. Emerging evidence suggests that microRNAs derived from the placenta circulate in the maternal blood during pregnancy and may be used as non-invasive biomarkers for pregnancy complications. With the intent to identify putative markers of fetal growth restriction(FGR) and new therapeutic druggable targets, we examined, in maternal blood samples, the expression of a group of microRNAs, known to be regulated by hypoxia. The expression of microRNAs was evaluated in maternal plasma samples collected from (1) women carrying a preterm FGR fetus(FGR group) or (2) women with an appropriately grown fetus matched at the same gestational age (Control group). To discriminate between early- and late-onset FGR, the study population was divided into two subgroups according to the gestational age at delivery. Four microRNAs were identified as possible candidate for the diagnosis of FGR: miR-16-5p, miR-103-3p, miR-107-3p, and miR-27b-3p that were upregulated in FGR blood samples before the 32nd week of gestation as compared to aged matched control group. By contrast, miRNA103-3p and miRNA107-3p analyzed between the 32nd and 37th week of gestation showed lower expression in the FGR group compared to aged matched controls. Notably, the expression of all miRNAs was increased through gestation in healthy control group. Our results showed that measurement of miRNAs in maternal blood may form the basis for a future diagnostic test to determine the degree of fetal hypoxia in FGR, thus allowing the start of appropriate therapeutic strategies in order to alleviate the burden of this disease

Data for: Integrating Machine Learning Techniques and Physiology Based Heart Rate Features for Antepartum Fetal Monitoring

The article contains a set of 12 linear and nonlinear indices extracted from Fetal Heart Rate (FHR) recordings by means of CTG monitors on two groups of fetuses: 60 normals and 60 Intra Uterine Growth Restricted (IUGR) fetuses. The two populations were selected by clinicians after birth on the basis of clinical standards for detecting growth restricted newborns (Apgar scores, percentile weight, …). The indices were computed on FHR recordings, each one lasting more than 30 minutes, by means of algorithms already published in the scientific literature.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Data for: Integrating Machine Learning Techniques and Physiology Based Heart Rate Features for Antepartum Fetal Monitoring

The article contains a set of 12 linear and nonlinear indices extracted from Fetal Heart Rate (FHR) recordings by means of CTG monitors on two groups of fetuses: 60 normals and 60 Intra Uterine Growth Restricted (IUGR) fetuses. The two populations were selected by clinicians after birth on the basis of clinical standards for detecting growth restricted newborns (Apgar scores, percentile weight, …). The indices were computed on FHR recordings, each one lasting more than 30 minutes, by means of algorithms already published in the scientific literature

Dataset on linear and non-linear indices for discriminating healthy and IUGR fetuses

The presented collection of data comprises of a set of 12 linear and nonlinear indices computed at different time scales and extracted from Fetal Heart Rate (FHR) traces acquired through Hewlett Packard CTG fetal monitors (series 1351A), connected to a PC. The sampling frequency of the recorded FHR signal is equal 2 Hz. The recorded populations consist of two groups of fetuses: 60 healthy and 60 Intra Uterine Growth Restricted (IUGR) fetuses. IUGR condition is a fetal condition defined as the abnormal rate of fetal growth. In clinical practice, diagnosis is confirmed at birth and may only be suspected during pregnancy. The pathology is a documented cause of fetal and neonatal morbidity and mortality. The described database was employed in a set of machine learning approaches for the early detection of the IUGR condition: “Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring” [1]. The added value of the proposed indices is their interpretability and close connection to physiological and pathological aspect of FHR regulation. Additional information on data acquisition, feature extraction and potential relevance in clinical practice are discussed in [1]

Dataset on linear and non-linear indices for discriminating healthy and IUGR fetuses

The presented collection of data comprises of a set of 12 linear and nonlinear indices computed at different time scales and extracted from Fetal Heart Rate (FHR) traces acquired through Hewlett Packard CTG fetal monitors (series 1351A), connected to a PC. The sampling frequency of the recorded FHR signal is equal 2 Hz. The recorded populations consist of two groups of fetuses: 60 healthy and 60 Intra Uterine Growth Restricted (IUGR) fetuses. IUGR condition is a fetal condition defined as the abnormal rate of fetal growth. In clinical practice, diagnosis is confirmed at birth and may only be suspected during pregnancy. The pathology is a documented cause of fetal and neonatal morbidity and mortality. The described database was employed in a set of machine learning approaches for the early detection of the IUGR condition: “Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring” [1]. The added value of the proposed indices is their interpretability and close connection to physiological and pathological aspect of FHR regulation. Additional information on data acquisition, feature extraction and potential relevance in clinical practice are discussed in [1]