Appearance-based localization using Group LASSO regression with an indoor experiment

We consider the problem of predicting survival times of cancer patients from the gene expression profiles of their tumor samples via linear regression modeling of log-transformed failure times. The partial least squares (PLS) and least absolute shrinkage and selection operator (LASSO) methodologies are used for this purpose where we first modify the data to account for censoring. Three approaches of handling right censored data-reweighting, mean imputation, and multiple imputation-are considered. Their performances are examined in a detailed simulation study and compared with that of full data PLS and LASSO had there been no censoring. A major objective of this article is to investigate the performances of PLS and LASSO in the context of microarray data where the number of covariates is very large and there are extremely few samples. We demonstrate that LASSO outperforms PLS in terms of prediction error when the list of covariates includes a moderate to large percentage of useless or noise variables; otherwise, PLS may outperform LASSO. For a moderate sample size (100 with 10,000 covariates), LASSO performed better than a no covariate model (or noise-based prediction). The mean imputation method appears to best track the performance of the full data PLS or LASSO. The mean imputation scheme is used on an existing data set on lung cancer. This reanalysis using the mean imputed PLS and LASSO identifies a number of genes that were known to be related to cancer or tumor activities from previous studies.

Features and interaction between features of liver disease is of great significance for the classification of liver disease. Based on least absolute shrinkage and selection operator (LASSO) and interaction LASSO, the generalized interaction LASSO model is proposed in this paper for liver disease classification and compared with other methods. Firstly, the generalized interaction logistic classification model was constructed and the LASSO penalty constraints were added to the interactive model parameters. Then the model parameters were solved by an efficient alternating directions method of multipliers (ADMM) algorithm. The solutions of model parameters were sparse. Finally, the test samples were fed to the model and the classification results were obtained by the largest statistical probability. The experimental results of liver disorder dataset and India liver dataset obtained by the proposed methods showed that the coefficients of interaction features of the model were not zero, indicating that interaction features were contributive to classification. The accuracy of the generalized interaction LASSO method is better than that of the interaction LASSO method, and it is also better than that of traditional pattern recognition methods. The generalized interaction LASSO method can also be popularized to other disease classification areas.

Variable selection in genome-wide association studies can be a daunting task and statistically challenging because there are more variables than subjects. We propose an approach that uses principal-component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) to identify gene-gene interaction in genome-wide association studies. A PCA was used to first reduce the dimension of the single-nucleotide polymorphisms (SNPs) within each gene. The interaction of the gene PCA scores were placed into LASSO to determine whether any gene-gene signals exist. We have extended the PCA-LASSO approach using the bootstrap to estimate the standard errors and confidence intervals of the LASSO coefficient estimates. This method was compared to placing the raw SNP values into the LASSO and the logistic model with individual gene-gene interaction. We demonstrated these methods with the Genetic Analysis Workshop 16 rheumatoid arthritis genome-wide association study data and our results identified a few gene-gene signals. Based on our results, the PCA-LASSO method shows promise in identifying gene-gene interactions, and, at this time we suggest using it with other conventional approaches, such as generalized linear models, to narrow down genetic signals.

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

We present a framework for hyperspectral image (HSI) analysis validation, specifically abundance fraction estimation based on HSI measurements of water soluble dye mixtures printed on microarray chips. In our work we focus on the performance of two algorithms, the Least Absolute Shrinkage and Selection Operator (LASSO) and the Spatial LASSO (SPLASSO). The LASSO is a well known statistical method for simultaneously performing model estimation and variable selection. In the context of estimating abundance fractions in a HSI scene, the “sparse” representations provided by the LASSO are appropriate as not every pixel will be expected to contain every endmember. The SPLASSO is a novel approach we introduce here for HSI analysis which takes the framework of the LASSO algorithm a step further and incorporates the rich spatial information which is available in HSI to further improve the estimates of abundance. In our work here we introduce the dye mixture platform as a new benchmark data set for hyperspectral biomedical image processing and show our algorithm’s improvement over the standard LASSO.

Exosomal lipids for classifying early and late stage non-small cell lung cancer

Regression models are a form of supervised learning methods that are important for machine learning, statistics, and general data science. Despite the fact that classical ordinary least squares (OLS) regression models have been known for a long time, in recent years there are many new developments that extend this model significantly. Above all, the least absolute shrinkage and selection operator (LASSO) model gained considerable interest. In this paper, we review general regression models with a focus on the LASSO and extensions thereof, including the adaptive LASSO, elastic net, and group LASSO. We discuss the regularization terms responsible for inducing coefficient shrinkage and variable selection leading to improved performance metrics of these regression models. This makes these modern, computational regression models valuable tools for analyzing high-dimensional problems.

The distinction between chondrodermatitis nodularis helicis (CNH) and hyperplastic actinic keratosis (HAK) on the ear can pose a diagnostic challenge. We aimed to identify histopathological characteristics that could distinguish between CNH and HAK on routine sections using penalized least absolute shrinkage and selection operator (LASSO) logistic regression analysis. Cases of CNH (n = 80) and HAK (n = 28) were analyzed for selected histopathological characteristics. Fisher's exact test and LASSO regression were performed. In univariate analyses, the following were significantly associated with CNH: ulceration, acanthosis, granular layer in the majority of the lesion, hypergranulosis at the periphery of the lesion, hyperkeratosis at the periphery of the lesion, hyperparakeratosis at the periphery of the lesion, fibrosis, increased blood vessels, vertically oriented blood vessels, and fibrin. A LASSO model excluding atypia found that fibrin, fibrosis, presence of granular layer, ulceration, and vertically oriented blood vessels were most predictive of CNH. Keratinized strap cells were not a significant predictor. We have identified features that may aid in differentiating these entities and demonstrated that a LASSO regression model can identify predictors that may improve diagnostic accuracy. Our results indicate that the highest diagnostic accuracy in this dilemma is dependent on obtaining biopsy specimens with visible dermis.

The successful use of machine learning (ML) for medical diagnostic purposes has prompted myriad applications in cancer image analysis. Particularly for hepatocellular carcinoma (HCC) grading, there has been a surge of interest in ML-based selection of the discriminative features from high-dimensional magnetic resonance imaging (MRI) radiomics data. As one of the most commonly used ML-based selection methods, the least absolute shrinkage and selection operator (LASSO) has high discriminative power of the essential feature based on linear representation between input features and output labels. However, most LASSO methods directly explore the original training data rather than effectively exploiting the most informative features of radiomics data for HCC grading. To overcome this limitation, this study marks the first attempt to propose a feature selection method based on LASSO with dictionary learning, where a dictionary is learned from the training features, using the Fisher ratio to maximize the discriminative information in the feature. This study proposes a LASSO method with dictionary learning to ensure the accuracy and discrimination of feature selection. Specifically, based on the Fisher ratio score, each radiomic feature is classified into two groups: the high-information and the low-information group. Then, a dictionary is learned through an optimal mapping matrix to enhance the high-information part and suppress the low discriminative information for the task of HCC grading. Finally, we select the most discrimination features according to the LASSO coefficients based on the learned dictionary. The experimental results based on two classifiers (KNN and SVM) showed that the proposed method yielded accuracy gains, compared favorably with another 5 state-of-the-practice feature selection methods.

PurposeThis paper develops a robust hospital readmission prediction framework by combining the feature selection algorithm and machine learning (ML) classifiers. The improved feature selection is proposed by considering the uncertainty in patient's attributes that leads to the output variable.Design/methodology/approachFirst, data preprocessing is conducted which includes how raw data is managed. Second, the impactful features are selected through feature selection process. It started with calculating the relational grade of each patient towards readmission using grey relational analysis (GRA) and the grade is used as the target values for feature selection. Then, the influenced features are selected using the Least Absolute Shrinkage and Selection Operator (LASSO) method. This proposed method is termed as Grey-LASSO feature selection. The final task is the readmission prediction using ML classifiers.FindingsThe proposed method offered good performances with a minimum feature subset up to 54–65% discarded features. Multi-Layer Perceptron with Grey-LASSO gave the best performance.Research limitations/implicationsThe performance of Grey-LASSO is justified in two readmission datasets. Further research is required to examine the generalisability to other datasets.Originality/valueIn designing the feature selection algorithm, the selection on influenced input variables was based on the integration of GRA and LASSO. Specifically, GRA is a part of the grey system theory, which was employed to analyse the relation between systems under uncertain conditions. The LASSO approach was adopted due to its ability for sparse data representation.

BackgroundThe ARCADIA (Atrial Cardiopathy and Antithrombotic Drugs in Prevention After Cryptogenic Stroke) trial found no benefit of anticoagulation for preventing recurrent stroke in patients with atrial cardiopathy. Data on AF monitoring across trial sites may provide context for the findings and knowledge about the current standard of care for poststroke monitoring.MethodsAt study visits, sites reported any preceding use of prolonged heart rhythm monitoring, classified as either external ambulatory monitors or implantable loop recorders. We used relative risk regression, least absolute shrinkage and selection operator (LASSO) regression, and survival analysis to explore patient characteristics associated with monitoring, the association between monitoring and AF detection, and the interaction between monitoring and study treatment effect on recurrent stroke.ResultsOf 1633 patients with monitoring data, 957 (58.6%) underwent prolonged monitoring: 567 (34.7%) external ambulatory monitor, 479 (29.3%) implantable loop recorder, and 89 (5.5%) both. The strongest predictors of monitoring were Hispanic ethnicity (standardized LASSO coefficient, −0.19 [risk ratio (RR), 0.66]), National Institutes of Health Stroke Scale score (LASSO, −0.15 [RR per point, 0.97]), left atrial diameter (LASSO, 0.13 [RR per cm, 1.09]), and serum hemoglobin (LASSO, −0.12 [RR per g/dL, 0.97]). At the site level, the median proportion of patients who underwent monitoring was 63% (interquartile range, 36%–92%). The site‐level proportion of patients with an implantable loop recorder was associated with greater likelihood of AF detection (RR, 3.9 [95% CI, 2.1–7.4]) but did not modify the trial treatment effect (P value for interaction, 0.99).ConclusionsIn the ARCADIA trial, which enrolled patients with cryptogenic stroke across the United States and Canada, nearly 60% of patients underwent prolonged heart rhythm monitoring. Use of implantable loop recorders was associated with greater likelihood of AF detection.

Abstrak&#x0D; Pertumbuhan ekonomi akhir-akhir ini menjadi lebih penting karena implementasinya secara luas, konsep pertumbuhan ekonomi merupakan ukuran penilaian ekonomi suatu negara atau regional. Data pertumbuhan ekonomi dalam penelitian ini yang diukur dengan Produk Domestik Regional Bruto (PDRB) rentan terhadap masalah asumsi klasik. Salah satunya adalah tidak adanya multikolinearitas yaitu korelasi atau hubungan yang tinggi diantara variabel independen. Multikolinearitas menjadi masalah dalam analisis regresi, terutama pada Ordinary Least Square (OLS) karena menyebabkan estimasi koefisien regresi menjadi tidak efisien. Salah satu metode untuk mengatasi multikolinearitas menggunakan Least Absolute Shrinkage and Selection Operator (LASSO). LASSO adalah metode regresi berganda yang digunakan untuk shrinkage yaitu menyusutkan koefisien taksiran mendekati angka nol dan selection operator yaitu menyeleksi variabel-variabel independen sehingga menghasilkan model dengan variabel terbaik karena kendala itu, LASSO dapat mengkecilkan koefisien menuju nol atau mengaturnya dengan tepat nol sehingga dapat melakukan pemilihan variabel juga. Berdasarkan Variance Inflation Factor (VIF), terdapat korelasi yang tinggi antar variabel independen, sehingga terdapat multikolinearitas pada data pertumbuhan ekonomi Provinsi Maluku 1999-2021 jika kita menggunakan OLS. Dalam penelitian ini, LASSO mengkecilkan dua koefisien penduga variabel independen menjadi tepat nol, sehingga variabel-variabel tersebut dianggap tidak memiliki pengaruh yang signifikan terhadap model.

Prediction system is one of the tools that can support business decision making. However, existing prediction systems often give high error rate and high computational complexity. In order to make an accurate prediction system while reducing computational complexity, we proposed works that combines multi-layered clustering model using k-means++ as a technique to model data, and feature selection LASSO to predict variables. K-means++ is chosen as clustering method because of its simplicity and its solution through poor initialization, while LASSO is chosen as feature selection because of its good accuracy and its linear nature that cause model complexity to be simpler than non-linear feature selection. First, k-means++ is applied in each layer, then centroids from each layer are transferred into a central processor. Next, a set of centroids is then proceeded using feature selection LASSO to predict variable. Simulation results show that the proposed prediction system has given a good prediction accuracy with MAPE 23.45% and has reduced computational complexity by 89.77%.

ObjectiveAlzheimer's disease, an irreversible neurological condition, demands timely diagnosis for effective clinical intervention. This study employs radiomics analysis to assess image features in default mode network cerebral perfusion imaging among individuals with cognitive impairment.MethodsA radiomics analysis of cerebral perfusion imaging was conducted on 117 patients with cognitive impairment. They were divided into training and validation sets in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest were employed to select and model image features, followed by logistic regression analysis of LASSO and Random Forest results. Diagnostic performance was assessed by calculating the area under the curve (AUC).ResultsIn the training set, LASSO achieved AUC of 0.978, Random Forest had an AUC of 0.933. In the validation set, LASSO had AUC of 0.859, Random Forest had AUC of 0.986. By conducting Logistic Regression analysis in combination with LASSO and Random Forest, we identified a total of five radiomics features, with four related to morphology and one to textural features, originating from the medial prefrontal cortex and middle temporal gyrus. In the training set, Logistic Regression achieved AUC of 0.911, while in the validation set, it attained AUC of 0.925.ConclusionThe medial prefrontal cortex and middle temporal gyrus are the two brain regions within the default mode network that hold the highest significance for Alzheimer's disease diagnosis. Radiomics analysis contributes to the clinical assessment of Alzheimer's disease by delving into image data to extract deeper layers of information.

ABSTRACTIn this paper, we show the validity of the adaptive least absolute shrinkage and selection operator (LASSO) procedure in estimating stationary autoregressive distributed lag(p,q) models with innovations in a broad class of conditionally heteroskedastic models. We show that the adaptive LASSO selects the relevant variables with probability converging to one and that the estimator is oracle efficient, meaning that its distribution converges to the same distribution of the oracle-assisted least squares, i.e., the least square estimator calculated as if we knew the set of relevant variables beforehand. Finally, we show that the LASSO estimator can be used to construct the initial weights. The performance of the method in finite samples is illustrated using Monte Carlo simulation.