Optimal Feature Subset Research Articles

Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control

This study proposes a novel Hybrid Metaheuristic with explicit diversity control, aimed at finding an optimal feature subset by thoroughly exploring the search space to prevent premature convergence. Background/Objectives: Unlike traditional evolutionary computing techniques, which only consider the best individuals in a population, the proposed strategy also considers the worst individuals under certain conditions. In consequence, feature selection frequencies tend to be more uniform, decreasing the probability of premature convergent results and local-optima solutions. Methods: An image database containing 608 images, evenly balanced between positive and negative coronary stenosis cases, was used for experiments. A total of 473 features, including intensity, texture, and morphological types, were extracted from the image bank. A Support Vector Machine was employed to classify positive and negative stenosis cases, with Accuracy and the Jaccard Coefficient used as performance metrics. Results: The proposed strategy achieved a classification rate of 0.92 for Accuracy and 0.85 for the Jaccard Coefficient, obtaining a subset of 16 features, which represents a discrimination rate of 0.97 from the 473 initial features. Conclusions: The Hybrid Metaheuristic with explicit diversity control improved the classification performance of coronary stenosis cases compared to previous literature. Based on the achieved results, the identified feature subset demonstrates potential for use in clinical practice, particularly in decision-support information systems.

Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles

With the growing popularity of autonomous vehicles (AVs), confirming their safety has become a significant concern. Vehicle manufacturers have combined the Android operating system into AVs to improve consumer comfort. However, the diversity and weaknesses of the Android operating system pose substantial safety risks to AVs, as these factors can expose them to threats, namely Android malware. The advanced behaviour of multi-data source fusion in autonomous driving models has mitigated recognition accuracy and effectualness for Android malware. To efficiently counter new malware variants, novel techniques distinct from conventional methods must be utilized. Machine learning (ML) techniques cannot detect every new and complex malware variant. The deep learning (DL) model is an efficient tool for detecting various malware variants. This manuscript proposes a Deep Learning-Based Improved Transformer Model on Android Malware Detection (DLBITM-AMD) technique for Internet vehicles (IoVs). The main aim of the presented DLBITM-AMD approach is to detect Android malware effectually and accurately. The DLBITM-AMD method performs a Z-score normalization process to convert the raw data into a standard form. Then, the DLBITM-AMD approach utilizes the binary grey wolf optimization (BGWO) model to select optimum feature subsets. An improved transformer is integrated with the RNN model and softmax to enhance classification for Android malware recognition. Finally, the snake optimizer algorithm (SOA) method is employed to select the optimum parameter for the classification method. An extensive experiment of the DLBITM-AMD method is accomplished on a benchmark dataset. The performance validation of the DLBITM-AMD technique portrayed a superior accuracy value of 99.26% over existing Android malware recognition models.

Explorative Binary Gray Wolf Optimizer with Quadratic Interpolation for Feature Selection.

The high dimensionality of large datasets can severely impact the data mining process. Therefore, feature selection becomes an essential preprocessing stage, aimed at reducing the dimensionality of the dataset by selecting the most informative features while improving classification accuracy. This paper proposes a novel binary Gray Wolf Optimization algorithm to address the feature selection problem in classification tasks. Firstly, the historical optimal position of the search agent helps explore more promising areas. Therefore, by linearly combining the best positions of the search agents, the algorithm's exploration capability is increased, thus enhancing its global development ability. Secondly, the novel quadratic interpolation technique, which integrates population diversity with local exploitation, helps improve both the diversity of the population and the convergence accuracy. Thirdly, chaotic perturbations (small random fluctuations) applied to the convergence factor during the exploration phase further help avoid premature convergence and promote exploration of the search space. Finally, a novel transfer function processes feature information differently at various stages, enabling the algorithm to search and optimize effectively in the binary space, thereby selecting the optimal feature subset. The proposed method employs a k-nearest neighbor classifier and evaluates performance through 10-fold cross-validation across 32 datasets. Experimental results, compared with other advanced algorithms, demonstrate the effectiveness of the proposed algorithm.

Feature Selection as Deep Sequential Generative Learning

Feature selection aims to identify the most pattern-discriminative feature subset. In prior literature, filter (e.g., backward elimination) and embedded (e.g., LASSO) methods have hyperparameters (e.g., top- k , score thresholding) and tie to specific models, thus, hard to generalize; wrapper methods search a feature subset in a huge discrete space and is computationally costly. To transform the way of feature selection, we regard a selected feature subset as a selection decision token sequence and reformulate feature selection as a deep sequential generative learning task that distills feature knowledge and generates decision sequences. Our method includes three steps: (1) We develop a deep variational transformer model over a joint of sequential reconstruction, variational, and performance evaluator losses. Our model can distill feature selection knowledge and learn a continuous embedding space to map feature selection decision sequences into embedding vectors associated with utility scores. (2) We leverage the trained feature subset utility evaluator as a gradient provider to guide the identification of the optimal feature subset embedding; (3) We decode the optimal feature subset embedding to autoregressively generate the best feature selection decision sequence with autostop. Extensive experimental results show this generative perspective is effective and generic, without large discrete search space and expert-specific hyperparameters. The code is available at http://tinyurl.com/FSDSGL .

FTDZOA: An Efficient and Robust FS Method with Multi-Strategy Assistance.

Feature selection (FS) is a pivotal technique in big data analytics, aimed at mitigating redundant information within datasets and optimizing computational resource utilization. This study introduces an enhanced zebra optimization algorithm (ZOA), termed FTDZOA, for superior feature dimensionality reduction. To address the challenges of ZOA, such as susceptibility to local optimal feature subsets, limited global search capabilities, and sluggish convergence when tackling FS problems, three strategies are integrated into the original ZOA to bolster its FS performance. Firstly, a fractional order search strategy is incorporated to preserve information from the preceding generations, thereby enhancing ZOA's exploitation capabilities. Secondly, a triple mean point guidance strategy is introduced, amalgamating information from the global optimal point, a random point, and the current point to effectively augment ZOA's exploration prowess. Lastly, the exploration capacity of ZOA is further elevated through the introduction of a differential strategy, which integrates information disparities among different individuals. Subsequently, the FTDZOA-based FS method was applied to solve 23 FS problems spanning low, medium, and high dimensions. A comparative analysis with nine advanced FS methods revealed that FTDZOA achieved higher classification accuracy on over 90% of the datasets and secured a winning rate exceeding 83% in terms of execution time. These findings confirm that FTDZOA is a reliable, high-performance, practical, and robust FS method.

Deep Learning Approach for Fetal Health Prediction

Monitoring the foetus's health is crucial during pregnancy to avoid complications that may worsen the course of pregnancy and delivery. Cardiotocography (CTG) is a tool that provides complex information by monitoring the foetus's heart rate signal. Obstetricians visually interpret these signals to predict potential risks and draw clinical inferences. The interpretation, however, relies on the expertise of the obstetrician, leading to a significant false positive rate. Thus, the study uses deep learning techniques to effectively identify foetal health states as 'Normal', 'Suspect' and 'Pathological'. The dataset used is CTG data drawn from the Kaggle repository. The optimal subset of features is obtained by comparing traditional feature selection and meta-heuristic-based feature selection techniques. Deep learning algorithms, namely, Convolution Neural Networks, Artificial Neural Networks, and Radial Basis Function Networks, are applied to train multi-class classification models that predict foetal health status. The models are then evaluated using performance metrics.

Generative Adversarial Autoencoder Integration Voting Algorithm Based on Mass Spectrometry Data

Mass spectrometry technology is often used in disease prevention and diagnosis and treatment. However, the number of mass spectrometry data features is huge and the features vary greatly between different diseases, making multi-disease diagnosis. The task of judgment is complex and difficult. To address the above problems, this paper proposes a generative adversarial autoencoder integrated voting algorithm msDAGVote based on mass spectrometry data.The generative adversarial network based on dual autoencoders is used as the feature extraction framework of msDAGVote. After inputting mass spectrometry data for training, the generator subnetwork is used for feature extraction.Finally, the constructed features were screened by integrating the voting feature selection algorithm, and the optimal feature subset was used for multi-disease diagnosis.The results were evaluated on mass spectrometry datasets of different disease types. The experimental data showed that the features extracted by msDAGVote outperformed the comparison methods and significantly reduced the classification time.The required number of features and excellent disease classification and diagnosis capabilities are required. The classification AUC exceeds 0.98 on 6 datasets and The set exceeds 0.87.

A novel brain network analysis method for pediatric ADHD using RFE-GA feature selection strategy

Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent childhood disorder, and related research has been increasing in recent years. However, it remains a challenging issue to accurately identify individuals with ADHD. The research proposes a method for ADHD detection using Recursive Feature Elimination-Genetic Algorithm (RFE-GA) for the feature selection of EEG data. Firstly, this study employed Transfer Entropy (TE) to construct brain networks from the EEG data of the ADHD and Normal groups, conducting an analysis of effective connectivity to unveil causal relationships in the brain's information exchange activities. Subsequently, a dual-layer feature selection method combining Recursive Feature Elimination (RFE) and Genetic Algorithm (GA) was proposed. Using the global search capability of GA and the feature selection ability of RFE, the performance of each feature subset is evaluated to find the optimal feature subset. Finally, a Support Vector Machine (SVM) classifier was employed to classify the ultimate feature set. The results revealed the control group exhibited lower connectivity strength in the left temporal alpha and beta bands, but higher frontal connectivity strength compared to the ADHD group. Additionally, in the gamma frequency band, the control group had higher top lobe connectivity strength than the ADHD group. Through the RFE-GA feature selection method, the optimized feature set was more concise, achieving classification accuracies of 91.3%, 94.1%, and 90.7% for the alpha, beta, and gamma frequency bands, respectively. The proposed RFE-GA feature selection method significantly reduced the number of features, thereby improving classification accuracy.&#xD.

Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions.

T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders. This study presents a novel theoretical method that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs. To evaluate the universality of our approach across different TCR systems, we utilized a dataset that includes peptide libraries tested against three distinct murine TCRs. A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key features contributing to binding affinity. Our analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR-peptide interactions. The results of our feature selection method are consistent with findings from hybrid approaches that utilize both sequence and structural data as input as well as experimental data. Our theoretical approach highlights the role of feature selection in peptide-TCR interactions, providing a powerful tool for uncovering the molecular mechanisms of the T-cell response and assisting in the design of more advanced targeted therapeutics.

IMOABC: An efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection

With the development of data science, the challenge of high-dimensional data has become increasingly prevalent. High-dimensional data contains a significant amount of redundant information, which can adversely affect the performance and effectiveness of machine learning algorithms. Therefore, it is necessary to select the most relevant features from the raw data and perform feature selection on high-dimensional data. In this paper, a novel filter–wrapper feature selection method based on an improved multi-objective artificial bee colony algorithm (IMOABC) is proposed to address the feature selection problem in high-dimensional data. This method simultaneously considers three objectives: feature error rate, feature subset ratio, and distance, effectively improving the efficiency of obtaining the optimal feature subset on high-dimensional data. Additionally, a novel Fisher Score-based initialization strategy is introduced, significantly enhancing the quality of solutions. Furthermore, a new dynamic adaptive strategy is designed, effectively improving the algorithm’s convergence speed and enhancing its global search capability. Comparative experimental results on microarray cancer datasets demonstrate that the proposed method significantly improves classification accuracy and effectively reduces the size of the feature subset when compared to various traditional and state-of-the-art multi-objective feature selection algorithms. IMOABC improves the classification accuracy by 2.27% on average compared to various multi-objective feature selection methods, while reducing the number of selected features by 88.76% on average. Meanwhile, IMOABC shows an average improvement of 4.24% in classification accuracy across all datasets, with an average reduction of 76.73% in the number of selected features compared to various traditional methods.

BYDSEX: Binary Young's double-slit experiment optimizer with adaptive crossover for feature selection: Investigating performance issues of network intrusion detection

Contemporary advancements in technology provide vast quantities of data with large dimensions, leading to high computing burdens. These big data quantities suffer from irrelevant, redundant, and noisy features. Hence, Feature Selection (FS) has become a crucial task to identify the optimal subsets of features. This research proposes a Binary version of Young's Double-Slit Experiment optimizer (BYDSE) with crossover operation (BYDSEX) for tackling FS issues. Furthermore, the proposed algorithm employs the V-shaped transfer function to convert continuous solutions generated by the standard YDSE into binary ones. To assess the new solutions, we employ a well-known wrapper approach, K-Nearest Neighbors (KNN), which uses the Euclidean distance metric. We integrate an adaptive crossover with a bitwise AND operation into the suggested algorithm to enhance its exploration and population diversity. Moreover, the bitwise AND operation transfers the most informative and beneficial features to the new solutions. We compared BYDSEX with nine of the most recent and powerful algorithms using 31 large-scale datasets to demonstrate its efficacy. Moreover, our BYDSEX optimizer is utilized to detect the DDoS attacks faced by most IoT devices and contemporary technologies, using six datasets extracted from CIC-DDoS2019 and NSL-KDD. Various performance metrics are utilized to assess the algorithms, such as the accuracy, the selected feature size the fitness values, the fitness values, and the time. Two statistical tests are carried out, like paired-samples T and the Wilcoxon signed-rank. BYDSEX achieved superior results compared to its competitors for most of the datasets. Furthermore, BYDSEX obtains average accuracy values of 99.78%, 99.89%, 99.69% and 99.48% for LDAP and MSSQL, NETBIOS and NSL-KDD, respectively.

Predicting creep life of CrMo pressure vessel steel using machine learning models with optimal feature subset selection

The data-driven approach for creep life prediction typically integrates numerous characteristics, including material compositions, manufacturing details, and service conditions, into machine learning models. In this study, a machine learning-based creep life prediction approach with optimal feature subset selection is established for 2.25Cr1Mo pressure vessel steel. Before model training and testing, six critical features that significantly impact the creep life of 2.25Cr1Mo steel are selected, specifically the applied stress, temperature, and chemical compositions consisting of Cr, Ni, Mn, and Mo. Various machine learning algorithms, along with the traditional L–M method, are utilized for model training and performance evaluation. Additionally, the developed models undergo validation using experimental data independent of the training and testing datasets to assess their generalization abilities. The results reveal that, among all tested models, the support vector regression (SVR) model, coupled with the optimal feature subset, demonstrates superior prediction accuracy and generalization capability. Finally, the creep life prediction model exhibiting optimal performance is deployed into a software application, leveraging the Python programming language. This predictor tool facilitates rapid and precise creep life predictions for 2.25Cr1Mo pressure vessel steel, relying solely on a limited amount of input information, and provides a clear and visual presentation of the prediction results.

Machine Learning based Lung Cancer Diagnostic System using Optimized Feature Subset Selection

Lung is a vital organ that plays a major role in respiration. Without breathing, one may not survive in this world. Hence lung is an important organ that acts as filter to absorb oxygen and supply it to heart where pumping takes place through blood vessel in the circulatory system .The pumped blood takes oxygen and other nutrients to every other parts of the body. Hence one must take care of lung. There are various diseases associated with lungs. Lung Cancer is a deadly disease that spread across the countries all over the world. An early detection of lung cancer has been proved to improve the survival rate of human life. There are various resources are available to detect the lung cancer disease. They are low dose CT-scans, X-rays, blood-based screening, pathology slide reading, biopsy’s test, survey data(clinical dataset) etc. helps to predict the disease well in advance. Our proposed work uses two clinical datasets that has various features to detect how likely the persons get affected from the lung disease. Dataset1 includes features such as age, gender, smoking, yellow fingers, anxiety, peer-pressure, chronic disease, fatigue, allergy, wheezing, alcohol, coughing, shortness of breath, swallowing difficulty, and chest pain. Also, the work has experimented with another dataset2 that represents causes of lung cancer due to exposure of pesticide. Our proposed diagnostic system consider all these features in total and perform feature selection to extract optimal feature subsets using cuckoo search algorithm then perform classification using machine learning algorithms such as Linear Support Vector Machine, Logistic Regression and Random Forest algorithm. It is observed that with the cuckoo search algorithm, dataset 1 achieves an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100% by LR Classifier. The Linear SVC classifier achieves an accuracy of 90%, a precision of 88%, a recall of 86%, and an F1-score of 87%.The Random forest Classifier achieves an accuracy of 91%, precision of 86%, recall of 93%, and F1-score of 90%. For dataset 2, both the LR classifier and Linear SVC classifier outperform with an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100%. Whereas Random Forest provides accuracy of 97%, precision of 97%, recall of 96%, and F1-score of 97%.

Wind power forecasting with metaheuristic-based feature selection and neural networks

Accurate forecasting of wind power generation is crucial for ensuring a stable and efficient energy supply, reducing the environmental impact of energy production, and promoting a cleaner and more sustainable energy supply. Inaccurate forecasts can lead to a mismatch between wind power generation and energy demand, resulting in wasted energy, increased emissions, and reduced grid stability. Therefore, improving the accuracy of wind power generation forecasting is essential for optimizing energy storage and grid management, reducing the reliance on fossil fuels, decreasing greenhouse gas emissions, and promoting a more sustainable energy future. This study proposes an innovative approach to enhance wind power generation forecasting accuracy by leveraging the strengths of metaheuristic algorithms for feature selection and integrating them with Neural Networks (NN). Specifically, five distinct algorithms - Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Teaching-Learning-Based Optimization (TLBO), and Evolutionary Mating Algorithm (EMA) - are integrated with NN model to identify optimal feature subsets from a comprehensive dataset of 18 diverse features. The results show that the GA consistently outperforms other algorithms in selecting the most influential features, leading to improved precision in wind power predictions. Notably, the GA achieves the best root mean square error (RMSE) of 37.1837 and the best mean absolute error (MAE) of 18.6313, outperforming the other algorithms and demonstrating the importance of feature selection in improving the accuracy of wind power forecasting. This innovative framework advances the field of renewable energy forecasting and provides valuable insights into optimizing feature sets for improved predictions across diverse domains.

A leader-adaptive particle swarm optimization with dimensionality reduction strategy for feature selection

Feature selection (FS) is a key data pre-processing method in machine learning tasks. It aims to obtain better classification accuracy of an algorithm with the smallest size of selected feature subset. Particle Swarm Optimization has been widely applied in FS tasks. However, when solving FS task on high-dimensional datasets, most of the PSO-based FS methods are easy to get premature convergence and fall into the local optimum. To address this issue, a leader-adaptive particle swarm optimization with dimensionality reduction strategy (LAPSO-DR) is proposed in this paper. Firstly, a hybrid initialization strategy based on feature importance is formulated. The population is divided into two parts, which have different initialization ranges. It can not only improve the diversity of the population but also eliminate some redundant features. Secondly, the leader-adaptive strategy is proposed to improve the exploitation ability of the population, in which each particle can have a different learning exemplar selected from the elite sub-swarm. Finally, the dimensionality reduction strategy based on Markov blanket is introduced to reduce the size of the optimal feature subset. LAPSO-DR is compared with 8 representative FS methods on 18 benchmark datasets. The experimental results show that LAPSO-DR can obtain smaller sizes of feature subsets with highest classification accuracies on 17 out of 18 datasets. The classification accuracies of LAPSO-DR are over 90% on 14 datasets and the feature elimination rates are higher than 60% on 18 datasets.

Extraction of Maize Distribution Information Based on Critical Fertility Periods and Active–Passive Remote Sensing

This study proposes a new method for integrating active and passive remote sensing data during critical reproductive periods in order to extract maize areas early and to address the problem of low accuracy in the classification of maize-growing areas affected by climate change. Focusing on Jiaozuo City, this study utilized active–passive remote sensing images to determine the optimal time for maize identification. The relative importance of features was assessed using a feature selection method combined with a machine learning algorithm, the impact of both single-source and multi-source features on accuracy was analyzed to generate the optimal feature subset, and the classification accuracies of different machine learning classification methods for maize at the tasseling stage were compared. Ultimately, this study identified the most effective remote sensing features and methods for maize detection during the optimal fertility period. The experimental results show that the feature set optimized for the tasseling stage significantly enhanced maize recognition accuracy. Specifically, the random forest (RF) method, when applied to the multi-source data fusion feature set, yielded the highest accuracy, improving classification accuracy by 24.6% and 4.86% over single-source features, and achieving an overall accuracy of 93.38% with a Kappa coefficient of 0.91. Data on the study area’s maize area were also extracted for the years 2018–2022, with accuracy values of 93.83%, 98.77%, 97%, and 98.05%, respectively.

Reflective Distributed Denial of Service Detection: A Novel Model Utilizing Binary Particle Swarm Optimization-Simulated Annealing for Feature Selection and Gray Wolf Optimization-Optimized LightGBM Algorithm.

The fast growth of the Internet has made network security problems more noticeable, so intrusion detection systems (IDSs) have become a crucial tool for maintaining network security. IDSs guarantee the normal operation of the network by tracking network traffic and spotting possible assaults, thereby safeguarding data security. However, traditional intrusion detection methods encounter several issues such as low detection efficiency and prolonged detection time when dealing with massive and high-dimensional data. Therefore, feature selection (FS) is particularly important in IDSs. By selecting the most representative features, it can not only improve the detection accuracy but also significantly reduce the computational complexity and attack detection time. This work proposes a new FS approach, BPSO-SA, that is based on the Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) algorithms. It combines these with the Gray Wolf Optimization (GWO) algorithm to optimize the LightGBM model, thereby building a new type of reflective Distributed Denial of Service (DDoS) attack detection model. The BPSO-SA algorithm enhances the global search capability of Particle Swarm Optimization (PSO) using the SA mechanism and effectively screens out the optimal feature subset; the GWO algorithm optimizes the hyperparameters of LightGBM by simulating the group hunting behavior of gray wolves to enhance the detection performance of the model. While showing great resilience and generalizing power, the experimental results show that the proposed reflective DDoS attack detection model surpasses conventional methods in terms of detection accuracy, precision, recall, F1-score, and prediction time.

Multi-population differential evolution approach for feature selection with mutual information ranking

Feature selection is a crucial aspect of data preprocessing because of the significant effect of redundant features on classification performance and the extensive computational resources required. Evolutionary algorithm-based feature-selection methods have shown remarkable results in determining the optimal feature subset. To further enhance classification performance, this paper proposes a novel multi-population differential evolution approach for feature selection with mutual information ranking (MI-MPODE). Firstly, population preprocessing guided by mutual information is employed to reduce the dimensionality of the initial feature space. Then, the feature subset obtained from mutual information serves as the initial population for MI-MPODE. MI-MPODE incorporates a novel multi-population information-sharing mechanism, with common individuals from three layers contributing to inter-subpopulation information sharing. Additionally, an individual enhancement strategy is proposed to handle the variations of individuals in the population, and a Lens imaging opposition-based learning method is adopted to improve the algorithm’s optimization capability. MI-MPODE is compared with several state-of-the-art evolutionary algorithm-based feature selection methods through experimental comparisons. The results show that MI-MPODE outperforms all comparison algorithms on more than half of the datasets, with a significant reduction in the number of features used, demonstrating a significant advantage over competitors.

Intelligent cardiovascular disease diagnosis using deep learning enhanced neural network with ant colony optimization

To identify patterns in big medical datasets and use Deep Learning and Machine Learning (ML) to reliably diagnose Cardio Vascular Disease (CVD), researchers are currently delving deeply into these fields. Training on large datasets and producing highly accurate validation results is exceedingly difficult. Furthermore, early and precise diagnosis is necessary due to the increased global prevalence of cardiovascular disease (CVD). However, the increasing complexity of healthcare datasets makes it challenging to detect feature connections and produce precise predictions. To address these issues, the Intelligent Cardiovascular Disease Diagnosis based on Ant Colony Optimisation with Enhanced Deep Learning (ICVD-ACOEDL) model was developed. This model employs feature selection (FS) and hyperparameter optimization to diagnose CVD. Applying a min–max scaler, medical data is first consistently prepared. The key feature that sets ICVD-ACOEDL apart is the use of Ant Colony Optimisation (ACO) to select an optimal feature subset, which in turn helps to upgrade the performance of the ensuring deep learning enhanced neural network (DLENN) classifier. The model reforms the hyperparameters of DLENN for CVD classification using Bayesian optimization. Comprehensive evaluations on benchmark medical datasets show that ICVD-ACOEDL exceeds existing techniques, indicating that it could have a significant impact on CVD diagnosis. The model furnishes a workable way to increase CVD classification efficiency and accuracy in real-world medical situations by incorporating ACO for feature selection, min–max scaling for data pre-processing, and Bayesian optimization for hyperparameter tweaking.

Sex-Specific Imaging Biomarkers for Parkinson's Disease Diagnosis: A Machine Learning Analysis.

This study aimed to identify sex-specific imaging biomarkers for Parkinson's disease (PD) based on multiple MRI morphological features by using machine learning methods. Participants were categorized into female and male subgroups, and various structural morphological features were extracted. An ensemble Lasso (EnLasso) method was employed to identify a stable optimal feature subset for each sex-based subgroup. Eight typical classifiers were adopted to construct classification models for PD and HC, respectively, to validate whether models specific to sex subgroups could bolster the precision of PD identification. Finally, statistical analysis and correlation tests were carried out on significant brain region features to identify potential sex-specific imaging biomarkers. The best model (MLP) based on the female subgroup and male subgroup achieved average classification accuracy of 92.83% and 92.11%, respectively, which were better than that of the model based on the overall samples (86.88%) and the overall model incorporating gender factor (87.52%). In addition, the most discriminative feature of PD among males was the lh 6r (FD), but among females, it was the lh PreS (GI). The findings indicate that the sex-specific PD diagnosis model yields a significantly higher classification performance compared to previous models that included all participants. Additionally, the male subgroup exhibited a greater number of brain region changes than the female subgroup, suggesting sex-specific differences in PD risk markers. This study underscore the importance of stratifying data by sex and offer insights into sex-specific variations in PD phenotypes, which could aid in the development of precise and personalized diagnostic approaches in the early stages of the disease.