Efficient Feature Selection Research Articles

Combined Ensemble Intrusion Detection Model using Deep learning with Feature Selection for Fog Computing Environments

Decentralized architecture known as fog computing is situated between the cloud and data-producing devices. It acts as a conduit between cloud services and IoT devices. In order to reduce latency, fog computing can handle a significant amount of computation for time-sensitive IoT applications. The Fog layer is simultaneously vulnerable to numerous assaults. To defend the fog nodes from attacks, fog computing paradigms may be suited for deep learning-based intrusion detection systems (IDS). In this paper, a combined Ensemble Intrusion Detection Model using Deep learning with Efficient Feature Selection using Random forests is proposed for Fog Computing Environments by using two deep learning models of traditional CNN and IDS-AlexNet model called Ensemble CNN-IDS with Random Forest and showed this model gives high accuracy of attack detection. The respective model implementations demonstrated on the UNSW-NB15 dataset that consists of 9 classes of attacks namely Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcodes and Worms. The proposed combined Ensemble Intrusion Detection Model using Deep learning with Efficient Feature Selection for intrusions detection is shown to be accurate and efficient by using different classifiers. Our proposed model provides high the accuracy in attack detection of about 97.5% that it outperformed various other traditional and recent models.

Classification of Music Genres using Feature Selection and Hyperparameter Tuning

The ability of music to spread joy and excitement across lives, makes it widely acknowledged as the human race's universal language. The phrase "music genre" is frequently used to group several musical styles together as following a shared custom or set of guidelines. According to their unique preferences, people now make playlists based on particular musical genres. Due to the determination and extraction of appropriate audio elements, music genre identification is regarded as a challenging task. Music information retrieval, which extracts meaningful information from music, is one of several real - world applications of machine learning. The objective of this paper is to efficiently categorise songs into various genres based on their attributes using various machine learning approaches. To enhance the outcomes, appropriate feature engineering and data pre-processing techniques have been performed. Finally, using suitable performance assessment measures, the output from each model has been compared. Compared to other machine learning algorithms, Random Forest along with efficient feature selection and hyperparameter tuning has produced better results in classifying music genres.

GeoClust: Feature engineering based framework for location-sensitive disaster event detection using AHP-TOPSIS

Disaster event detection aims to identify events like terrorist attacks, fire incidents, stampede incidents, building collapse, etc., reported in the online news articles or social media. Place of occurrence of disaster event is a significant feature associated with events for location-sensitive disaster event detection. Efficient feature selection and their augmentation with location information can contribute towards the evolution of traditional approaches and their adoption for location-sensitive disaster event detection leading to improvement in the overall process as a whole. Since the evaluation of event detection techniques deliberates various intrinsic and extrinsic performance metrics, the decision-making for the selection of feature sets is treated as a Multiple-Criteria Decision Making (MCDM) problem. This paper proposes a framework, GeoClust, that is based on feature engineering of traditional textual features in order to enhance their capability for improved location-sensitive disaster event detection. The framework augments context-free and context-based textual feature sets with feature sets of place of occurrence of the events and evaluates their performance using unsupervised machine learning algorithms for various performance metrics. Finally, the best feature set is selected using AHP-TOPSIS technique of MCDM in order to tune the system for automatic and efficient location-sensitive disaster event detection in real-time. Extensive set of experiments have been performed in order to evaluate the framework on a dataset of online news articles reporting disaster events about terrorist attacks, fire incidents, stampede incidents, building collapse and maoist attacks happened at different locations in India. The results show that the location-augmented feature sets significantly improve performance of location-sensitive disaster event detection as compared with traditional feature sets. The results also demonstrate that the context-based feature sets with location-augmentation are ranked higher than the context-free feature sets in MCDM analysis.

Wrapper feature selection method based differential evolution and extreme learning machine for intrusion detection system

The intrusion detection system (IDS) has gained a rapid increase of interest due to its widely recognized potential in various security fields, however, it suffers from several challenges. Different network datasets have several redundant and irrelevant features that affect the decision of the IDS classifier. Therefore, it is essential to decrease these features to improve the system performance. In this paper, an efficient wrapper feature selection method is proposed for improving the performance and decreasing the processing time of the IDS. The proposed approach employs a differential evaluation algorithm to select the useful features whilst the extreme learning machine classifier is applied after feature selection to evaluate the selected features. Many experiments are performed using the full NSL-KDD dataset to evaluate the performance of the proposed method. The results prove that the proposed approach can efficiently reduce the features, increase the accuracy, reduce the false alarm rates, and improve the processing time of the IDS in comparison to other recent related works.

Advanced Feature-Selection-Based Hybrid Ensemble Learning Algorithms for Network Intrusion Detection Systems

As cyber-attacks become remarkably sophisticated, effective Intrusion Detection Systems (IDSs) are needed to monitor computer resources and to provide alerts regarding unusual or suspicious behavior. Despite using several machine learning (ML) and data mining methods to achieve high effectiveness, these systems have not proven ideal. Current intrusion detection algorithms suffer from high dimensionality, redundancy, meaningless data, high error rate, false alarm rate, and false-negative rate. This paper proposes a novel Ensemble Learning (EL) algorithm-based network IDS model. The efficient feature selection is attained via a hybrid of Correlation Feature Selection coupled with Forest Panelized Attributes (CFS–FPA). The improved intrusion detection involves exploiting AdaBoosting and bagging ensemble learning algorithms to modify four classifiers: Support Vector Machine, Random Forest, Naïve Bayes, and K-Nearest Neighbor. These four enhanced classifiers have been applied first as AdaBoosting and then as bagging, using the aggregation technique through the voting average technique. To provide better benchmarking, both binary and multi-class classification forms are used to evaluate the model. The experimental results of applying the model to CICIDS2017 dataset achieved promising results of 99.7%accuracy, a 0.053 false-negative rate, and a 0.004 false alarm rate. This system will be effective for information technology-based organizations, as it is expected to provide a high level of symmetry between information security and detection of attacks and malicious intrusion.

Machine-learning effective many-body potentials for anisotropic particles using orientation-dependent symmetry functions.

Spherically symmetric atom-centered descriptors of atomic environments have been widely used for constructing potential or free energy surfaces of atomistic and colloidal systems and to characterize local structures using machine learning techniques. However, when particle shapes are non-spherical, as in the case of rods and ellipsoids, standard spherically symmetric structure functions alone produce imprecise descriptions of local environments. In order to account for the effects of orientation, we introduce two- and three-body orientation-dependent particle-centered descriptors for systems composed of rod-like particles. To demonstrate the suitability of the proposed functions, we use an efficient feature selection scheme and simple linear regression to construct coarse-grained many-body interaction potentials for computationally efficient simulations of model systems consisting of colloidal particles with an anisotropic shape: mixtures of colloidal rods and non-adsorbing polymer coils, hard rods enclosed by an elastic microgel shell, and ligand-stabilized nanorods. We validate the machine-learning (ML) effective many-body potentials based on orientation-dependent symmetry functions by using them in direct coexistence simulations to map out the phase behavior of colloidal rods and non-adsorbing polymer coils. We find good agreement with the results obtained from simulations of the true binary mixture, demonstrating that the effective interactions are well described by the orientation-dependent ML potentials.

Machine Learning-Based Intrusion Detection System: An Experimental Comparison

Recently, networks are moving toward automation and getting more and more intelligent. With the advent of big data and cloud computing technologies, lots and lots of data are being produced on the internet. Every day, petabytes of data are produced from websites, social media sites, or the internet. As more and more data are produced, a continuous threat of network attacks is also growing. An intrusion detection system (IDS) is used to detect such types of attacks in the network. IDS inspects packet headers and data and decides whether the traffic is anomalous or normal based on the contents of the packet. In this research, ML techniques are being used for intrusion detection purposes. Feature selection is also used for efficient and optimal feature selection. The research proposes a hybrid feature selection technique composed of the Pearson correlation coefficient and random forest model. For the machine learning (ML) model, decision tree, AdaBoost, and K-nearest neighbor are trained and tested on the TON_IoT dataset. The dataset is new and contains new and recent attack types and features. For deep learning (DL), multilayer perceptron (MLP) and long short-term memory are trained and tested. Evaluation is done on the basis of accuracy, precision, and recall. It is concluded from the results that the decision tree for ML and MLP for DL provides optimal accuracy with fewer false-positive and false-negative rates. It is also concluded from the results that the ML techniques are effective for detecting intrusion in the networks.

Multilabel Feature Selection Based on Relative Discernibility Pair Matrix

In multilabel learning, the curse of dimensionality is one of major challenges. Existing single-label feature selection methods cannot be directly applied to multilabel data, and multilabel feature selections have thus been widely studied. As an effective granular computing tool, rough set theory has been applied to multilabel feature selections for addressing various realistic applications. However, existing rough set-based methods not only cannot effectively characterize the ability of features to distinguish multilabel sample pairs, but also usually own high time complexity. In this article, we propose two novel multilabel feature selection methods from the perspective of discerning sample pairs. First, relative discernibility pair matrixes of features are defined in the framework of fuzzy rough set, where each element represents the degree of distinguishing the corresponding sample pair by features. On this basis, a novel evaluation measure of feature subsets is defined. Afterwards, a heuristic multilabel feature selection approach titled RDPM based on the proposed measure is put forward. Inspired by sampling and ensemble strategies, another efficient and robust multilabel feature selection approach titled RDPM_SE is proposed as well. Finally, extensive experiments on 13 real-world multilabel datasets are conducted, and experimental results show that the proposed algorithms outperform seven state-of-the-art methods in terms of performances and the running time.

Hybrid feature selection based on SLI and genetic algorithm for microarray datasets.

One of the major problems in microarray datasets is the large number of features, which causes the issue of “the curse of dimensionality” when machine learning is applied to these datasets. Feature selection refers to the process of finding optimal feature set by removing irrelevant and redundant features. It has a significant role in pattern recognition, classification, and machine learning. In this study, a new and efficient hybrid feature selection method, called Garank&rand, is presented. The method combines a wrapper feature selection algorithm based on the genetic algorithm (GA) with a proposed filter feature selection method, SLI-γ. In Garank&rand, some initial solutions are built regarding the most relevant features based on SLI-γ, and the remaining ones are only the random features. Eleven high-dimensional and standard datasets were used for the accuracy evaluation of the proposed SLI-γ. Additionally, four high-dimensional well-known datasets of microarray experiments were used to carry out an extensive experimental study for the performance evaluation of Garank&rand. This experimental analysis showed the robustness of the method as well as its ability to obtain highly accurate solutions at the earlier stages of the GA evolutionary process. Finally, the performance of Garank&rand was also compared to the results of GA to highlight its competitiveness and its ability to successfully reduce the original feature set size and execution time.

Dynamic Fire Risk Classification Prediction of Stadiums: Multi-Dimensional Machine Learning Analysis Based on Intelligent Perception

Stadium fires can easily cause massive casualties and property damage. The early risk prediction of stadiums will be able to reduce the incidence of fires by making corresponding fire safety management and decision making in an early and targeted manner. In the field of building fires, some studies apply data mining techniques and machine learning algorithms to the collected risk hazard data for fire risk prediction. However, most of these studies use all attributes in the dataset, which may degrade the performance of predictive models due to data redundancy. Furthermore, machine learning algorithms are numerous and applied to fewer stadium fires, and it is crucial to explore models suitable for predicting stadium fire risk. The purpose of this study was to identify salient features to build a model for predicting stadium fire risk predictions. In this study, we designed an index attribute threshold interval to classify and quantify different fire risk data. We then used Gradient Boosting-Recursive Feature Elimination (GB-RFE) and Pearson correlation analysis to perform efficient feature selection on risk feature attributes to find the most informative salient feature subsets. Two cross-validation strategies were employed to address the dataset imbalance problem. Using the smart stadium fire risk data set provided by the Wuhan Emergency Rescue Detachment, the optimal prediction model was obtained based on the identified significant features and six machine learning methods of 12 combination forms, and full features were input as an experimental comparison study. Five performance evaluation metrics were used to evaluate and compare the combined models. Results show that the best performing model had an F1 score of 81.9% and an accuracy of 93.2%. Meanwhile, by introducing a precision-recall curve to explain the actual classification performance of each model, AdaBoost achieves the highest Auprc score (0.78), followed by SVM (0.77), which reveals more stable performance under such imbalanced data.

Fusing entropy measures for dynamic feature selection in incomplete approximation spaces

The selection of informative and discriminative features from incomplete dynamic data has emerged as an essential problem because the data preparation in modern applications is a dynamic updating process, and the collected data are often fraught with missing or unobserved values. Using the effectiveness of entropy measures to quantify uncertainty information in incomplete approximation spaces under the framework of tolerance rough sets, a novel incremental feature selection approach is presented in this study from an information-theoretic perspective. Based on the updating patterns of tolerance classification and decision partition induced by conditional and decision features, a novel fused representation of entropy measures in an incomplete approximation space is proposed to accelerate the calculation of feature significance during the heuristic search process. A computationally efficient feature selection algorithm is developed by integrating the incremental fusing strategy of entropy when the data increases dynamically in size. Numerical experiments were performed on several benchmark datasets with data updating for different data sizes and incremental ratios to demonstrate the effectiveness of our method. The superiority of the proposed method is established extensively in terms of computational efficiency, cardinality and accuracy of the selected feature subset.

Efficient selection of SAR features using ML based algorithms for accurate FVC estimation

Fractional vegetation cover (FVC) is a ratio of vertical projection area of green vegetation to the total area under consideration. Crops infested by pests, diseases or nutrient deficiency show their impact on the crop coverage. Therefore, FVC is a good indicator of crop health and arid soil. Recently, various models have been reported for FVC estimation using optical data, but it is still limited to different weather conditions. Therefore, it is not feasible to continue crop monitoring using optical data. On the other hand, synthetic aperture radar (SAR) data is weather independent, and has a good potential for crop monitoring in all-weather conditions. SAR data has been used for many crop parameters estimation, however has not been much explored for FVC estimation. Plenty SAR features are available which are sensitive to vegetation parameters. Some of the features are sensitive during early crop stages (e.g., entropy, DpRVI-dual polarization radar vegetation index), while others are sensitive during different stages of crops (backscattering signal of VH and VV polarization). Therefore, there is a need to critically assess all the features and find the optimum combination that provides exemplary results during the entire crop cycle. For this purpose, sixteen features are considered using the different combinations of Sentinel-1 SLC data and their temporal analysis is observed for their different phenology stages. Four machine learning (ML) based models i.e., LightGBM, Xgboost, K-nearest neighbor (KNN), and Random Forest have been explored on these features for FVC estimation. The performance of each model is assessed with the error metrics. Xgboost emerges as the best model with a minimum RMSE value of 0.159. Xgboost model has the capability to recognize the most important features. Due to the stochastic nature of the algorithm, feature priority sequence may vary, therefore, algorithm runs multiple times and the probability of each feature for every position is calculated and on the basis of the highest probability, feature importance sequences is decided. Xgboost model is developed by increasing the input features in the order of their importance sequence and the RMSE value is calculated for each input combination. It is noted that initially, the RMSE value improved from 0.22 to 0.15 for the top five input features. When additional features were included, no further improvement in the RMSE was observed. Therefore; entropy, alpha, VH, VH/VV, and VV are the top five features which are used in the Xgboost model for FVC estimation instead of all sixteen features, which delivers satisfactory results.

Optimized deep learning‐based intrusion detection for wireless sensor networks

SummaryThe technological innovations and wide use of Wireless Sensor Network (WSN) applications need to handle diverse data. These huge data possess network security issues as intrusions that cannot be neglected or ignored. An effective strategy to counteract security issues in WSN can be achieved through the Intrusion Detection System (IDS). IDS ensures network integrity, availability, and confidentiality by detecting different attacks. Regardless of efforts by various researchers, the domain is still open to obtain an IDS with improved detection accuracy with minimum false alarms to detect intrusions. Machine learning models are deployed as IDS, but their potential solutions need to be improved in terms of detection accuracy. The neural network performance depends on feature selection, and hence, it is essential to bring an efficient feature selection model for better performance. An optimized deep learning model has been presented to detect different types of attacks in WSN. Instead of the conventional parameter selection procedure for Convolutional Neural Network (CNN) architecture, a nature‐inspired whale optimization algorithm is included to optimize the CNN parameters such as kernel size, feature map count, padding, and pooling type. These optimized features greatly improved the intrusion detection accuracy compared to Deep Neural network (DNN), Random Forest (RF), and Decision Tree (DT) models.

An Enhanced Evolutionary Based Feature Selection Approach Using Grey Wolf Optimizer for the Classification of High-dimensional Biological Data

Feature selection (FS) is a pre-processing step that aims to eliminate the redundant and less-informative features to enhance the performance of data mining techniques. It is also considered as one of the key success factors for classification problems in high-dimensional datasets. This paper proposes an efficient wrapper feature selection method based on Grey Wolf Optimizer (GWO). GWO is a recent metaheuristic algorithm that has been widely employed to solve diverse optimization problems. However, GWO mainly follows the search directions toward the leading wolves, making it prone to fall into local optima, especially when dealing with high-dimensional problems, which is the case when dealing with many biological datasets. An enhanced variation of GWO called EGWO, which adapts two enhancements, is introduced to overcome this specific shortcoming. In the first place, the transition parameter concept is incorporated to move GWO from the exploration phase to the exploitation phase. Several adaptive non-linear decreasing formulas are introduced to control the transition parameters. In the second place, a random-based search strategy is exploited to empower diversity during the search process. Two binarization schemes using S-shaped and V-shaped transfer functions are incorporated to map the continuous search space into a binary one for FS. The efficiency of the proposed EGWO is validated on ten high-dimensional low-samples biological data. Our experiments show the promising performance of EGWO compared to the original GWO approach and other state-of-the-art techniques in terms of dimensionality reduction and the enhancement of classification performance.

Ultra-selective tin oxide-based chemiresistive gas sensor employing signal transform and machine learning techniques

Selective detection of gases has been a major concern among metal-oxide based chemiresistive gas sensors due to their intrinsic cross-sensitivity. In this endeavor, we report integration of single metal-oxide based chemiresistive sensor with different soft computing tools to obtain perfect recognition of tested analyte molecules by means of signal processing, feature extraction and machine learning. The fabricated sensor device consists of SnO2 hollow-spheres as the sensing material, which was synthesized chemically. A remarkable gas sensing performance has been observed towards every target volatile organic compound (VOC); which exhibits the sensor having cross-sensitivity. The transient response curves obtained from the sensor were processed using fast Fourier transform (FFT) and discrete wavelet transform (DWT) to squeeze out distinct characteristic features associated with each tested VOC. The signal transform tools were taken in a comparative fashion to examine their credibility in terms of feature extraction and assistance for pattern recognition. The extracted features were assigned as input information to the machine learning algorithms in a supervised manner to discriminate among the tested VOCs qualitatively. Moreover, a quantitative estimation of concentration for corresponding VOCs was also obtained with acceptable accuracy. The main highlight of the paper is the vigilant and efficient selection of features from the transformed signal which adequately allows the machine learning algorithms to achieve excellent classification (best average accuracy: 96.84%) and quantification. The collective results promote a step towards the realization of an automated and real-time detection.

Retraction Note to: A hybrid metaheuristic approach for efficient feature selection methods in big data

Retraction Note to: A hybrid metaheuristic approach for efficient feature selection methods in big data

An efficient feature selection method based on improved elephant herding optimization to classify high‐dimensional biomedical data

AbstractMachine learning algorithms are widely applied to biomedical data to classify the samples of patients and healthy persons. The high‐dimensional biomedical datasets contain a large number of features to represent a sample. However, such datasets may have redundant, noisy and irrelevant features, influencing machine learning algorithms' classification performance and increasing computation overhead. Therefore, data normalization and feature selection techniques are introduced to reduce the impact of noisy features and accurately identify the patterns in features to improve the predictive accuracy of diagnosis. This work proposes an efficient feature selection and parameter optimization method to classify high‐dimensional biomedical datasets. In the proposed method, a binary version of the improved elephant herding optimization (IEHO) algorithm is introduced to select features and optimize the C and γ parameters of the support vector machine classifier. Further, four variants of the proposed method are presented based on data normalization techniques: Z‐score normalization (ZN), Pareto‐scaling (PS), tan h‐based normalization (TN), and variant of tan h‐based normalization (VTN). The proposed variants reduce the dominance of noisy features and explore the feature space to obtain the optimal feature set that maximizes the classification accuracy and minimizes the time complexity. The performance of the proposed variants is evaluated on 15 high‐dimensional biomedical datasets. Friedman's mean rank test is applied to check the statistical difference between proposed variants. Results show that the proposed Z‐score normalization‐IEHO (ZN‐IEHO) variant performed significantly better than the other proposed variants for classification accuracy, false‐positive rate and f‐score metrics. Moreover, the performance of the proposed ZN‐IEHO variant is compared with 18 state‐of‐the‐art feature selection methods. The experimental results expressed the effectiveness of the proposed ZN‐IEHO variant in finding the best combination of features and parameters to classify the biomedical datasets accurately.

IPReditor-CMG: Improving a predictive RNA editor for crop mitochondrial genomes using genomic sequence features and an optimal support vector machine

In crops, RNA editing is one of the most important post-transcriptional processes in which specific cytidines (C) in virtually all mitochondrial protein-coding genes are converted to uridines (U). Despite extensive recent research in RNA editing, exploring all of the C-to-U editing events efficiently on the genomic scale remains challengeable. Developing accurate prediction methods for the detection of RNA editing sites would dramatically reduce experimental determination. Therefore, we propose a novel method, iPReditor-CMG (improved predictive RNA editor for crop mitochondrial genomes), to predict crop mitochondrial editing sites using genome sequence and an optimised support vector machine (SVM). We first selected three mitochondrial genomes with known RNA editing sites from Arabidopsis thaliana, Brassica napus and Oryza sativa, released by NCBI, as the training and test sets. The genes and their transcripts from self-sequenced tobacco mitochondrial ATPase were selected as the validation set. The iPReditor-CMG first coded the genome sequences as numerical vectors and then performed an efficient feature selection on the high-dimensional feature space, where the SVM was employed in feature selection and following modelling. The average independent prediction accuracy of intraspecific editing sites across three species was 0.85, and up to 0.91 in A. thaliana, which outperformed the reference models. For the interspecific independent prediction, the prediction accuracy between dicotyledons was 0.78 and the accuracy between dicotyledons and monocotyledons was 0.56, which implies that there might be similarity in the C-to-U editing mechanism in close relatives. Finally, the best model was identified with an independent test accuracy of 0.91 and an AUC of 0.88, which suggested that five unreported feature sequences, i.e. TGACA, ACAAC, GTAGA, CCGTT and TAACA, are closely associated with the editing phenomenon. Multiple tests supported that the iPReditor-CMG could be effectively applied to predict editing sites in crop mitochondria, which may further contribute to understanding the mechanisms of site editing and post-transcriptional events in crop mitochondria.

Efficient analysis of COVID-19 clinical data using machine learning models.

Because of the rapid spread of COVID-19 to almost every part of the globe, huge volumes of data and case studies have been made available, providing researchers with a unique opportunity to find trends and make discoveries like never before by leveraging such big data. This data is of many different varieties and can be of different levels of veracity, e.g., precise, imprecise, uncertain, and missing, making it challenging to extract meaningful information from such data. Yet, efficient analyses of this continuously growing and evolving COVID-19 data is crucial to inform — often in real-time — the relevant measures needed for controlling, mitigating, and ultimately avoiding viral spread. Applying machine learning-based algorithms to this big data is a natural approach to take to this aim since they can quickly scale to such data and extract the relevant information in the presence of variety and different levels of veracity. This is important for COVID-19 and potential future pandemics in general. In this paper, we design a straightforward encoding of clinical data (on categorical attributes) into a fixed-length feature vector representation and then propose a model that first performs efficient feature selection from such representation. We apply this approach to two clinical datasets of the COVID-19 patients and then apply different machine learning algorithms downstream for classification purposes. We show that with the efficient feature selection algorithm, we can achieve a prediction accuracy of more than 90% in most cases. We also computed the importance of different attributes in the dataset using information gain. This can help the policymakers focus on only certain attributes to study this disease rather than focusing on multiple random factors that may not be very informative to patient outcomes.Graphical abstract

Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques.

Cervical cancer has become the third most common form of cancer in the in-universe, after the widespread breast cancer. Human papillomavirus risk of infection is linked to the majority of cancer cases. Preventive care, the most expensive way of fighting cancer, can protect about 37% of cancer cases. The Pap smear examination is a standard screening procedure for the initial screening of cervical cancer. However, this manual test procedure generates many false-positive outcomes due to individual errors. Various researchers have extensively investigated machine learning (ML) methods for classifying cervical Pap cells to enhance manual testing. The random forest method is the most popular method for anticipating features from a high-dimensional cancer image dataset. However, the random forest method can get too slow and inefficient for real-time forecasts when too many decision trees are used. This research proposed an efficient feature selection and prediction model for cervical cancer datasets using Boruta analysis and SVM method to deal with this challenge. A Boruta analysis method is used. It is improved from of random forest method and mainly discovers feature subsets from the data source that are significant to assigned classification activity. The proposed model's primary aim is to determine the importance of cervical cancer screening factors for classifying high-risk patients depending on the findings. This research work analyses cervical cancer and various risk factors to help detect cervical cancer. The proposed model Boruta with SVM and various popular ML models are implemented using Python and various performance measuring parameters, i.e., accuracy, precision, F1–Score, and recall. However, the proposed Boruta analysis with SVM performs outstanding over existing methods.