Multi-feature Vehicle Detection Using Feature Selection

We develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) in the classification of the pulmonary lesion and identify optimal machine learning methods. This retrospective analysis included 201 patients (143 malignancies, 58 benign lesions). Radiomics features were extracted from multiparametric MRI, including T2-weighted imaging (T2WI), T1-weighted imaging (TIWI), and apparent diffusion coefficient (ADC) map. Three feature selection methods, including recursive feature elimination (RFE), t test, and least absolute shrinkage and selection operator (LASSO), and three classification methods, including linear discriminate analysis (LDA), support vector machine (SVM), and random forest (RF) were used to distinguish benign and malignant pulmonary lesions. Performance was compared by AUC, sensitivity, accuracy, precision, and specificity. Analysis of performance differences in three randomly drawn cross-validation sets verified the stability of the results. For most single MR sequences or combinations of multiple MR sequences, RFE feature selection method with SVM classifier had the best performance, followed by RFE with RF. The radiomics model based on multiple sequences showed a higher diagnostic accuracy than single sequence for every machine learning method. Using RFE with SVM, the joint model of T1WI, T2WI, and ADC showed the highest performance with AUC = 0.88 ± 0.02 (sensitivity 83%; accuracy 82%; precision 91%; specificity 79%) in test set. Quantitative radiomics features based on multiparametric MRI have good performance in differentiating lung malignancies and benign lesions. The machine learning method of RFE with SVM is superior to the combination of other feature selection and classifier methods. • Radiomics approach has the potential to distinguish between benign and malignant pulmonary lesions. • Radiomics model based on multiparametric MRI has better performance than single-sequence models. • The machine learning methods RFE with SVM perform best in the current cohort.

Chronic kidney disease (CKD) involves numerous variables, but only a few significantly impact the classification task. The statistically equivalent signature (SES) method, inspired by constraint-based learning of Bayesian networks, is employed to identify essential features in CKD. Unlike conventional feature selection methods, which typically focus on a single set of features with the highest predictive potential, the SES method can identify multiple predictive feature subsets with similar performance. However, most feature selection (FS) classifiers perform suboptimally with strongly correlated data. The FS approach faces challenges in identifying crucial features and selecting the most effective classifier, particularly in high-dimensional data. This study proposes using the Least Absolute Shrinkage and Selection Operator (LASSO) in conjunction with the SES method for feature selection in CKD identification. Following this, an ensemble deep-learning model combining long short-term memory (LSTM) and gated recurrent unit (GRU) networks is proposed for CKD classification. The features selected by the hybrid feature selection method are fed into the ensemble deep-learning model. The model’s performance is evaluated using accuracy, precision, recall, and F1 score metrics. The experimental results are compared with individual classifiers, including decision tree (DT), Random Forest (RF), logistic regression (LR), and support vector machine (SVM). The findings indicate a 2% improvement in classification accuracy when using the proposed hybrid feature selection method combined with the LSTM and GRU ensemble deep-learning model. Further analysis reveals that certain features, such as HEMO, POT, bacteria, and coronary artery disease, contribute minimally to the classification task. Future research could explore additional feature selection methods, including dynamic feature selection that adapts to evolving datasets and incorporates clinical knowledge to enhance CKD classification accuracy further.

High-dimensional datasets can lead to overfitting and computationally expensive model building on machine learning. This study uses a dimensionality reduction technique, namely feature selection techniques, to overcome these problems. Five feature selection methods were used, i.e., Chi-Square (CS), Information Gain (IG), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Least Absolute Shrinkage and Selection Operator (LASSO), and three classifier methods viz. Naïve Bayes, Extreme Gradient Boosting (XGB), and RF Classifier. The dataset used is the Heart Attack Analysis & Prediction Dataset. In this study, three scenarios of the best feature selection were carried out, namely: 1. selection of the best feature using a specific feature selection, 2. the intersection of selection of the best feature from the same category, 3. the intersection of selection of the best feature from the five proposed feature selection methods. The performance model is measured using accuracy, precision, recall, f1-score, AUC, and training time. This study reveals that feature selection is very effective in improving the performance of prediction models. Based on the experiment results, the best feature selection is CS and IG in the Filter Category with the XGB model. The best feature selected improved the performance of accuracy, precision, recall, f1-score, and AUC, i.e., 1.7%, 1%, 2.3%, 1.6%, and 0.2%, respectively. Meanwhile, training time requirements decreased by 23.5%. Feature selection with specific techniques performs better than feature selection by selecting the best features from the same category feature selection technique or various other feature selection methods.

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.

Pancreatic adenocarcinoma is one of the most aggressive and lethal cancers, with a poor prognosis primarily due to late-stage diagnosis. Improving the accuracy of pancreatic cancer diagnosis is crucial for enhancing survival outcomes, yet the sensitivity of conventional diagnostic methods remains a significant challenge. This study aims to evaluate the effectiveness of radiomics features extracted from Computed Tomography (CT) imaging, combined with machine learning models, for the detection of pancreatic adenocarcinoma. A retrospective dataset from Baqiyatallah Hospital, Tehran, Iran (2024) of 100 participants (50 with pancreatic adenocarcinoma (primarily stages II-III) and 50 healthy controls) was used. CT images were acquired with a three-phase protocol, and radiomics features were extracted using 3D Slicer software. Three classifiers-Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF)-were employed, with feature selection methods including Recursive Feature Elimination (RFE), Mutual Information (MI), and Least Absolute Shrinkage and Selection Operator (LASSO). Model performance was assessed using accuracy, precision, sensitivity, F1 score, and area under the curve (AUC). The SVM classifier with LASSO feature selection achieved the highest performance, with an accuracy of 0.83 and an AUC of 0.89. LR and RF also demonstrated strong results, with LASSO providing the best feature selection for both classifiers. SHAP analysis revealed that textural features such as gray-level-non-uniformity and run-length-non-uniformity were the most important drivers for distinguishing pancreatic cancer from normal tissue. Radiomics-based machine learning models show promise for improving the diagnosis of pancreatic adenocarcinoma. The combination of LASSO and powerful classifiers such as SVM, LR, and RF offers a robust framework for non-invasive, accurate diagnostic tools.

ObjectivesParkinson’s disease (PD) is the second most common neurodegenerative disorder; it affects more than 10 million people worldwide. Detecting PD usually requires a professional assessment by an expert, and investigation of the voice as a biomarker of the disease could be effective in speeding up the diagnostic process.MethodsWe present our methodology in which we distinguish PD patients from healthy controls (HC) using a large sample of 18,210 smartphone recordings. Those recordings were processed by an audio processing technique to create a final dataset of 80,594 instances and 138 features from the time, frequency, and cepstral domains. This dataset was preprocessed and normalized to create baseline machine-learning models using four classifiers, namely, linear support vector machine, K-nearest neighbor, random forest, and extreme gradient boosting (XGBoost). We divided our dataset into training and held-out test sets. Then we used stratified 5-fold cross-validation and four performance measures: accuracy, sensitivity, specificity, and F1-score to assess the performance of the models. We applied two feature selection methods, analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO), to reduce the dimensionality of the dataset by selecting the best subset of features that maximizes the performance of the classifiers.ResultsLASSO outperformed ANOVA with almost the same number of features. With 33 features, XGBoost achieved a maximum accuracy of 95.31% on training data, and 95.78% by predicting unseen data.ConclusionsDeveloping a smartphone-based system that implements machine-learning techniques is an effective way to diagnose PD using the voice as a biomarker.

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.

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.

Survival Prediction In High Dimensional Datasets – Comparative Evaluation Of Lasso Regularization and Random Survival Forests

To construct and validate a prediction model based on full-sequence MRI for preoperatively evaluating the invasion depth of bladder cancer. A total of 445 patients with bladder cancer were divided into a seven-to-three training set and test set for each group. The radiomic features of lesions were extracted automatically from the preoperative MRI images. Two feature selection methods were performed and compared, the key of which are the Least Absolute Shrinkage and Selection Operator (LASSO) and the Max Relevance Min Redundancy (mRMR). The classifier of the prediction model was selected from six advanced machine-learning techniques. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were applied to assess the efficiency of the models. The models with the best performance for pathological invasion prediction and muscular invasion prediction consisted of LASSO as the feature selection method and random forest as the classifier. In the training set, the AUC of the pathological invasion model and muscular invasion model were 0.808 and 0.828. Furthermore, with the mRMR as the feature selection method, the external invasion model based on random forest achieved excellent discrimination (AUC, 0.857). The full-sequence models demonstrated excellent accuracy for preoperatively predicting the bladder cancer invasion status. This study introduces a full-sequence MRI model for preoperative prediction of the depth of bladder cancer infiltration, which could help clinicians to recognise pathological features associated with tumour infiltration prior to invasive procedures. • Full-sequence MRI prediction model performed better than Vesicle Imaging-Reporting and Data System (VI-RADS) for preoperatively evaluating the invasion status of bladder cancer. • Machine learning methods can extract information from T1-weighted image (T1WI) sequences and benefit bladder cancer invasion prediction.

<title>Abstract</title> Objective To construct various radiomics models based on CT venous phase images for the quantitative assessment of gastric cancer (GC) invasion depth into the gastric wall, and to compare the diagnostic performance of different models in distinguishing early (T1-T2 stage) from advanced (T3-T4 stage) GC, aiming to identify the optimal model and evaluate its potential for clinical application. Methods In this retrospective study, 223 pathologically confirmed GC patients (66 early-stage, 157 advanced-stage) treated between January 2022 and May 2025 were enrolled. Patients were allocated into a training set (n = 156) and an independent test set (n = 67) through stratified random sampling at a 7:3 ratio. All patients underwent enhanced CT within one week prior to surgery. Venous phase images were selected, and three-dimensional regions of interest (ROIs) encompassing the tumor were manually delineated using 3D Slicer for radiomics feature extraction. In the training set, feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Four machine learning models were subsequently constructed: Logistic Regression (LR), Random Forest (RF), XGBoost (XGB), and Support Vector Machine (SVM). Model optimization was conducted via 5-fold cross-validation. Performance was evaluated using receiver operating characteristic (ROC) curves, precision-recall (PR) curves, confusion matrices, and decision curve analysis (DCA). Results A total of 148 radiomics features were extracted, from which 11 key features were selected by LASSO. In the training set under 5-fold cross-validation, the LR, RF, SVM, and XGB models demonstrated strong discriminatory ability, with AUCs of 0.912, 0.910, 0.899, and 0.880, respectively. In the independent test set, the LR model exhibited the best overall performance: AUC 0.912 (95% CI: 0.853–0.948), accuracy 0.791, sensitivity 0.787, specificity 0.800, F1-score 0.841, and average precision (AP) 0.966. DeLong's test revealed statistically significant differences in AUC among models (all P &lt; 0.001), though the difference between LR and RF was minimal (0.0021, 95% CI: − 0.0459–0.0502). All models showed good calibration (Hosmer–Lemeshow test P &gt; 0.05). DCA indicated that the LR model provided higher net clinical benefit within a threshold probability range of 0.3–0.8. Conclusion Among the multiple radiomics models constructed based on CT venous phase images, the LR model demonstrated the best performance in distinguishing early from advanced GC T stages, showing favorable diagnostic efficacy and clinical utility. It can serve as a quantitative auxiliary tool for preoperative staging and treatment decision-making, providing an objective basis for precise GC diagnosis and treatment.

Identification of potential biomarkers for Polygonum multiflorum-induced liver injury in a clinical cohort: integrating machine learning of metabolomics with transcriptomic profiling.

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

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%.

Comparative study of feature selection methods for wind speed estimation at ungauged locations