Proposed Feature Selection Algorithm Research Articles

Feature Selection Method based on Chaotic Salp Swarm Algorithm and Extreme Learning Machine for Network Intrusion Detection Systems

Network Intrusion Detection Systems (IDSs) have been widely used to monitor and manage network connections and prevent unauthorized connections. Machine learning models have been utilized to classify the connections into normal connections or attack connections based on the users' behavior. One of the most common issues facing the IDSs is the detection system's low classification accuracy and high dimensionality in the feature selection process. However, the feature selection methods are usually used to decrease the datasets' redundancy and enhance the classification performance. In this paper, a Chaotic Salp Swarm Algorithm (CSSA) was integrated with the Extreme Learning Machine (ELM) classifier to select the most relevant subset of features and decrease the dimensionality of a dataset. Each Salp in the population was represented in a binary form, where 1 represented a selected feature, while 0 represented a removed feature. The proposed feature selection algorithm was evaluated based on NSL-KDD dataset, which consists of 41 features. The results were compared with others and have shown that the proposed algorithm succeeded in achieving classification accuracy up to 97.814% and minimized the number of selected features.

Localization of Rolling Element Faults Using Improved Binary Particle Swarm Optimization Algorithm for Feature Selection Task

The accurate localization of the rolling element failure is very important to ensure the reliability of rotating machinery. This paper proposes an efficient and anti-noise fault diagnosis model for rolling elements. The proposed model is composed of feature extraction, feature selection and fault classification. Feature extraction is composed of signal processing and signal noise reduction. Signal processing is carried out by local mean decomposition (LMD), and signal noise reduction is performed by product function (PF) selection and wavelet packet decomposition (WPD). Through the steps of signal noise reduction, high-frequency noise can be effectively removed, and the fault information hidden under the noise can be extracted. To further improve the effectiveness of the diagnostic model, an improved binary particle swarm optimization (IBPSO) is proposed to find the most important features from the feature space. In IBPSO, cycling time-varying inertia weight is introduced to balance exploitation and exploration and improve the capability to escape from local solutions, and crossover and mutation operations are also introduced to improve exploration and exploitation capabilities, respectively. The main contributions of this research are briefly described as follows: (1) The feature extraction process applied in this research can effectively remove noise and establish a high-accuracy feature set. (2) The proposed feature selection algorithm has higher accuracy than the other state-of-the-art feature selection algorithms. (3) In a strong noise environment, the proposed rolling element fault diagnosis model is compared with the state-of-the-art fault diagnosis model in terms of classification accuracy. Experimental results show that the model can maintain high classification accuracy in a strong noise environment. Therefore, it can be proved that the fault diagnosis model proposed in this paper can be effectively applied to the fault diagnosis of rotating machinery.

Metaheuristic Search Based Feature Selection Methods for Classification of Cancer

Cancer is a cluster of diseases caused due to unusual cell growth. This paper aims to discover cancer prediction from the microarray gene expression data using the selected features. The metaheuristic search algorithms select the global and local optimal features using population and neighbourhood based algorithms. Although the ant colony optimization and genetic algorithm search for the global optimal features from the dataset entails enhanced classification, sometimes there occur some challenges in the selection of neighbourhood features. Against this background, two feature selection algorithms are proposed to hybridize tabu search, a neighbourhood based search algorithm with global optimal feature selection algorithm. Those are (1) Ant Colony Optimization and Tabu search with Fuzzy Rough set for Optimal feature selection (ACTFRO) algorithm, (2) Genetic algorithm and Tabu search with Fuzzy Rough set for Optimal feature selection (GATFRO) algorithm. The performance of proposed feature selection algorithms is assessed through a fuzzy rough nearest neighbour classifier using ten-fold cross validation. Four cancer medical datasets and one non-medical dataset are used to analyse the performance of the proposed algorithms in terms of classification accuracy, computation time, sensitivity, specificity, f-measure, receiver operation characteristics and positive predicted value. Results derived from the different performance metrics confirm that the proposed algorithms evidence effective global and local feature selection hybridization with improved results.

Classification of transfer modes in gas metal arc welding using acoustic signal analysis

Gas metal arc welding (GMAW) is a welding process in which an electric arc is formed between a wire electrode and a metal workpiece alongside a shielding gas to protect the arc from contaminants. There are several ways in which the molten electrode droplet can be transferred to the weld pool known as metal transfer modes. Identifying the metal transfer mode automatically is essential to monitor and control the welding process, especially in automated processes employed in modern Industry 4.0 manufacturing lines. However, limited research on this topic has been found in literature. This paper explores the automatic classification of metal transfer modes in GMAW based on machine learning techniques with various signals from the welding process, including acoustics, current, voltage and gas flow rate signals. Time and frequency domain features are first extracted from these signals and are used in a support vector machine classifier to detect the metal transfer modes. A feature selection algorithm is proposed to improve the prediction rate from 80 to 99% when all four signals are utilised. When only the non-intrusive acoustic signal is used, the prediction rates with and without the proposed feature selection algorithm are approximately 96% and 81%, respectively. The high prediction rate demonstrates the feasibility and promising accuracy of the acoustic signal–based classification method for future smart welding technology with real-time adaptive feedback control of the welding process.

Neighborhood rough sets with distance metric learning for feature selection

Neighborhood rough set is a useful mathematic tool to describe uncertainty in mixed data. Feature selection based on neighborhood rough set has been studied widely. However, most existing methods use a single predefined distance function to construct neighborhood granules. As not all datasets are created with the same way and data are also often disturbed with noisy, the same distance function may not be optimal for all datasets. This paper aims at improving the discriminative ability and decreasing the uncertainty in the representation from neighborhood rough set to deal with this issue. In this paper, distance learning method is first introduced into neighborhood rough set to optimize the structure of information granules. A novel neighborhood rough set model is then proposed, called Neighborhood rough set Model based on Distance metric learning (NMD). NMD exploits distance metric learning in which samples from the same decision achieve small distance than samples from different decisions. Such a method can improve the consistency of neighborhood granules. The paper also presents the properties of NMD and formulates the importance of feature. In addition, two feature selection algorithms are built upon the proposed NMD. Experimental results on real-world datasets demonstrate the effectiveness of the proposed feature selection algorithms and their superiority against comparison baselines.

Supervised feature selection using integration of densest subgraph finding with floating forward–backward search

In this paper, a novel approach of supervised feature selection is proposed based on the principle of dense subgraph discovery. To exploit dense subgraph discovery for the purpose of feature selection, the dataset is initially mapped to an equivalent weighted graph notation by considering the set of all features as its vertex set and the mutual dependency between each pair of features as the weight of the corresponding edge. The proposed feature selection algorithm proceeds in a two-phase manner. In the first stage, a dense sub-graph is first discovered so that the features within it become maximally non-redundant among each other and the averaged class relevance as well as averaged standard deviation of all these features are obtained as maximal as possible. In this regard, a novel induced degree is also defined for each feature by incorporating the aforesaid three important objectives of feature selection. In this phase, a modified version of an existing approximation algorithm is also used to find dense subgraph module. Finally, in the second stage, a floating forward–backward search is performed on the dense subgraph so obtained to reveal a better feature subset. In both stages, an existing version of the normalized mutual information score is employed to compute both the class relevance and redundancy. The main contribution of this paper is proposing a feature selection strategy by which the reduced features have the characteristics like maximal average class relevance, minimal average pairwise redundancy, and good discriminating power. The experimental results demonstrate that the proposed approach is competent with several conventional as well as state-of-art algorithms of supervised feature selection.

A scalable framework for large time series prediction

Knowledge discovery systems are nowadays supposed to store and process very large data. When working with big time series, multivariate prediction becomes more and more complicated because the use of all the variables does not allow to have the most accurate predictions and poses certain problems for classical prediction models. In this article, we present a scalable prediction process for large time series prediction, including a new algorithm for identifying time series predictors, which analyses the dependencies between time series using the mutual reinforcement principle between Hubs and Authorities of the Hits (Hyperlink-Induced Topic Search) algorithm. The proposed framework is evaluated on 3 real datasets. The results show that the best predictions are obtained using a very small number of predictors compared to the initial number of variables. The proposed feature selection algorithm shows promising results compared to widely known algorithms, such as the classic and the kernel principle component analysis, factor analysis, and the fast correlation-based filter method, and improves the prediction accuracy of many time series of the used datasets.

Robust Automatic Modulation Recognition Through Joint Contribution of Hand-Crafted and Contextual Features

Automatic modulation recognition (AMR) has become increasingly important in the field of signal processing, especially with the advancements of intelligent communication systems. Deep Learning (DL) technologies have been incorporated into the AMR field and they have shown outstanding performances against conventional AMR methods. The robustness of DL-based AMR methods under varying noise regimes is one of major concerns for the widespread utilization of this technology. Furthermore, most existing works have neglected the contributions of hand-crafted features (HCFs) in boosting the classification performances of DL-based AMR methods. In order to address the aforementioned technical challenges, a novel and robust DL-AMR method is proposed by leveraging the benefits of both contextual features (CFs) and HCFs for a specific range of signal-to-noise ratio (SNR). A novel feature selection algorithm is also proposed to search for the optimal sets of HCFs in order to reduce the dimensions of feature vectors without losing any important and relevant features. Simulation studies are performed to investigate the feasibility of proposed method in classifying 11 types of modulation schemes. Extensive performance analyses revealed the superiority of proposed method over baseline method in terms of the classification performance as well as the excellent capability of proposed feature selection algorithm in determining an optimal subset of HCFs.

Bacterial colony algorithm with adaptive attribute learning strategy for feature selection in classification of customers for personalized recommendation

This paper investigates a new bacterial colony-based feature selection algorithm to improve the classification accuracy of customers for personalized products recommendation. An attribute learning strategy is developed in this study to update the feature related population. Specifically, the features can be weighted according to their historic contributions to both the individual- and group-based subsets. Additionally, the frequency of appearance is also recorded for the feature candidates to improve the diversity of feature distribution and avoid the over-fitting. Based on the weight-based feature indexes and frequency of appearance records, the performance of feature subsets are enhanced by replacing the features being repeatedly appeared in a same vector. To explore the feasibility of the proposed method for the missing feature problems, the objective of the optimization is to minimize the classification error using the acceptable number of features. K-Nearest Neighbor is employed as the learning technique to cooperate with the proposed feature selection method. The effectiveness of the proposed feature selection method is demonstrated by performing test on the datasets from UCI machine learning repository and real-world data from Amazon customer reviews of products. Compared with other seven feature selections methods, the proposed feature selection algorithm outperforms the other algorithms by achieving higher classification accuracy rate using smaller features.

Clustering-Based Band Selection Using Structural Similarity Index and Entropy for Hyperspectral Image Classification

Despite the unique capabilities of hyperspectral images for classification tasks, handling the high dimension of these data is challenging. Therefore, dimension reduction algorithms have been proposed to solve this challenge. In this paper, an unsupervised Feature Selection (FS) algorithm was proposed for hyperspectral image classification. First, the entropy values of hyperspectral bands were employed to identify and remove noisy bands. Afterward, the Structural Similarity (SSIM) index and the k-means clustering algorithm were combined to select a few representative bands. Subsequently, the selected bands were injected into a supervised classifier, and the obtained Overall Accuracy (OA) and Kappa Coefficient (KC) were used to evaluate the performance of the proposed method. Finally, the results were compared with the ones achieved from other well-known and state-of-the-art FS approaches. The results revealed that the proposed method outperformed other FS algorithms. Furthermore, the proposed FS algorithm obtained equal or higher OA and KC in comparison with the case of employing all hyperspectral bands. Additionally, a stability analysis step was performed to investigate the consistency of the proposed method. The results suggest the potential of the FS approach for hyperspectral image classification.

Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal

BackgroundDetecting human drowsiness during some critical works like vehicle driving, crane operating, mining blasting, etc. is one of the safeguards to prevent accidents. Among several drowsiness detection (DD) methods, a combination of neuroscience and computer science knowledge has a better ability to differentiate awake and sleep states. Most of the current models are implemented using multi-sensors electroencephalogram (EEG) signals, multi-domain features, predefined features selection algorithms. Therefore, there is great interest in the method of detecting drowsiness on embedded platforms with improved accuracy using generalized best features. New-methodSingle-channel EEG based drowsiness detection (DD) model is proposed in this by utilizing wavelet packet transform (WPT) to extract the time-domain features from considered channel EEG. The dimension of the feature vector is reduced by the proposed novel feature selection method. ResultsThe proposed model on freely available real-time sleep analysis EEG and Simulated Virtual Driving Driver (SVDD) EEG achieves 94.45% and 85.3% accuracy, respectively. Comparison-with-existing-methodThe results show that the proposed DD method produces better accuracy compared to the state-of-the-art using the physiological dataset with the proposed time-domain sub-band-based features and feature selection method. This task of detecting drowsiness by analyzing the 5-seconds EEG signal with four features is an improvement to my previous work on detecting drowsiness using a 30-seconds EEG signal with 66 features. ConclusionsTime-domain features obtained from EEG time-domain sub-bands collected using WPT achieving excellent accuracy rate by selecting unique optimization features for all subjects by the proposed feature selection algorithm.

Automatic text classification using machine learning and optimization algorithms

In the recent years, the volume of text documents in the form of digital way has grown up extremely in size. As significance, there is a need to be competent to automatically bring together and classify the documents based on their content. The main goal of text classification is to partition the unstructured set of documents into their respective categories based on its content. The main aim of this research work is to automatically classify the documents which are stored in the personal computer into their relevant categories. This work has two significant phases. In the first phase, the important features are selected for classification and the second phase is the classification of text documents. For selecting the optimal features, this research work proposes a new algorithm, optimization technique for feature selection (OTFS) algorithm. To estimate the proficiency of proposed feature selection algorithm, the OTFS algorithm was compared with the existing approaches artificial bee colony, firefly algorithm, ant colony optimization and particle swarm optimization. In the second phase, this research work proposed machine learning-based automatic text classification (MLearn-ATC) algorithm for text classification. In classification, the MLearn-ATC algorithm was compared with widely used classification techniques probabilistic neural network, support vector machine, K-nearest neighbor and Naive Bayes. From this, the output of first phase is used as the input for classification phase. The decisive results establish that the proposed algorithms achieve the better accuracy for optimizing the features and classifying the text documents based on their content.

Revealing False Positive Features in Epileptic EEG Identification.

Feature selection plays a vital role in the detection and discrimination of epileptic seizures in electroencephalogram (EEG) signals. The state-of-the-art EEG classification techniques commonly entail the extraction of the multiple features that would be fed into classifiers. For some techniques, the feature selection strategies have been used to reduce the dimensionality of the entire feature space. However, most of these approaches focus on the performance of classifiers while neglecting the association between the feature and the EEG activity itself. To enhance the inner relationship between the feature subset and the epileptic EEG task with a promising classification accuracy, we propose a machine learning-based pipeline using a novel feature selection algorithm built upon a knockoff filter. First, a number of temporal, spectral, and spatial features are extracted from the raw EEG signals. Second, the proposed feature selection algorithm is exploited to obtain the optimal subgroup of features. Afterwards, three classifiers including [Formula: see text]-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) are used. The experimental results on the Bonn dataset demonstrate that the proposed approach outperforms the state-of-the-art techniques, with accuracy as high as 99.93% for normal and interictal EEG discrimination and 98.95% for interictal and ictal EEG classification. Meanwhile, it has achieved satisfactory sensitivity (95.67% in average), specificity (98.83% in average), and accuracy (98.89% in average) over the Freiburg dataset.

Fast feature selection for interval-valued data through kernel density estimation entropy

Kernel density estimation, which is a non-parametric method about estimating probability density distribution of random variables, has been used in feature selection. However, existing feature selection methods based on kernel density estimation seldom consider interval-valued data. Actually, interval-valued data exist widely. In this paper, a feature selection method based on kernel density estimation for interval-valued data is proposed. Firstly, the kernel function in kernel density estimation is defined for interval-valued data. Secondly, the interval-valued kernel density estimation probability structure is constructed by the defined kernel function, including kernel density estimation conditional probability, kernel density estimation joint probability and kernel density estimation posterior probability. Thirdly, kernel density estimation entropies for interval-valued data are proposed by the constructed probability structure, including information entropy, conditional entropy and joint entropy of kernel density estimation. Fourthly, we propose a feature selection approach based on kernel density estimation entropy. Moreover, we improve the proposed feature selection algorithm and propose a fast feature selection algorithm based on kernel density estimation entropy. Finally, comparative experiments are conducted from three perspectives of computing time, intuitive identifiability and classification performance to show the feasibility and the effectiveness of the proposed method.

Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data.

Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient’s clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment.

Weighted ReliefF with threshold constraints of feature selection for imbalanced data classification

SummaryFeature selection is a useful method for fulfilling the data classification since the inherent heterogeneity of data and the redundancy of features are often encountered in the current data exploding era. Some commonly used feature selection algorithms, which include but are not limited to Pearson, maximal information coefficient, and ReliefF, are well‐posed under the assumption that instances are distributed homogenously in datasets. However, such an assumption might be not true in the practice. As such, in the presence of data imbalance, these traditional feature selection algorithms might be invalid due to their prejudices to the minority class, which includes few samples. The purpose of the addressed problem in this article is to develop an effective feature selection algorithm for imbalanced judicial datasets, which is capable of extracting essential features while deleting negligible ones according to the practical feature requirements. To achieve this goal, the number and the distribution of samples in each class are fully taken into consideration for the correlation analysis. Compared with the traditional feature selection algorithms, the proposed improved ReliefF algorithm is equipped with: (i) different weights of features according to the characteristics of heterogeneous samples in different classes; (ii) justice for imbalanced datasets; and (iii) threshold constraints resulting from the practical feature requirements. Finally, experiments on a judicial dataset and six public datasets well illustrate the effectiveness and the superiority of the proposed feature selection algorithm in improving the classification accuracy for imbalanced datasets.

Association between work-related features and coronary artery disease: A heterogeneous hybrid feature selection integrated with balancing approach

Coronary artery disease (CAD) is a leading cause of death worldwide and is associated with high healthcare expenditure. Researchers are motivated to apply machine learning (ML) for quick and accurate detection of CAD. The performance of the automated systems depends on the quality of features used. Clinical CAD datasets contain different features with varying degrees of association with CAD. To extract such features, we developed a novel hybrid feature selection algorithm called heterogeneous hybrid feature selection (2HFS). In this work, we used Nasarian CAD dataset, in which work place and environmental features are also considered, in addition to other clinical features. Synthetic minority over-sampling technique (SMOTE) and Adaptive synthetic (ADASYN) are used to handle the imbalance in the dataset. Decision tree (DT), Gaussian Naive Bayes (GNB), Random Forest (RF), and XGBoost classifiers are used. 2HFS-selected features are then input into these classifier algorithms. Our results show that, the proposed feature selection method has yielded the classification accuracy of 81.23% with SMOTE and XGBoost classifier. We have also tested our approach with other well-known CAD datasets: Hungarian dataset, Long-beach-va dataset, and Z-Alizadeh Sani dataset. We have obtained 83.94%, 81.58% and 92.58% for Hungarian dataset, Long-beach-va dataset, and Z-Alizadeh Sani dataset, respectively. Hence, our experimental results confirm the effectiveness of our proposed feature selection algorithm as compared to the existing state-of-the-art techniques which yielded outstanding results for the development of automated CAD systems.

Pulsars detection by machine learning with very few features

ABSTRACTIt is an active topic to investigate the schemes based on machine learning (ML) methods for detecting pulsars as the data volume growing exponentially in modern surveys. To improve the detection performance, input features into an ML model should be investigated specifically. In the existing pulsar detection researches based on ML methods, there are mainly two kinds of feature designs: the empirical features and statistical features. Due to the combinational effects from multiple features, however, there exist some redundancies and even irrelevant components in the available features, which can reduce the accuracy of a pulsar detection model. Therefore, it is essential to select a subset of relevant features from a set of available candidate features and known as feature selection. In this work, two feature selection algorithms –Grid Search (GS) and Recursive Feature Elimination (RFE) – are proposed to improve the detection performance by removing the redundant and irrelevant features. The algorithms were evaluated on the Southern High Time Resolution University survey (HTRU-S) with five pulsar detection models. The experimental results verify the effectiveness and efficiency of our proposed feature selection algorithms. By the GS, a model with only two features reach a recall rate as high as 99 per cent and a false positive rate (FPR) as low as 0.65 per cent; by the RFE, another model with only three features achieves a recall rate of 99 per cent and an FPR of 0.16 per cent in pulsar candidates classification. Furthermore, this work investigated the number of features required as well as the misclassified pulsars by our models.

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images.

Diagnosis is a critical preventive step in Coronavirus research which has similar manifestations with other types of pneumonia. CT scans and X-rays play an important role in that direction. However, processing chest CT images and using them to accurately diagnose COVID-19 is a computationally expensive task. Machine Learning techniques have the potential to overcome this challenge. This article proposes two optimization algorithms for feature selection and classification of COVID-19. The proposed framework has three cascaded phases. Firstly, the features are extracted from the CT scans using a Convolutional Neural Network (CNN) named AlexNet. Secondly, a proposed features selection algorithm, Guided Whale Optimization Algorithm (Guided WOA) based on Stochastic Fractal Search (SFS), is then applied followed by balancing the selected features. Finally, a proposed voting classifier, Guided WOA based on Particle Swarm Optimization (PSO), aggregates different classifiers’ predictions to choose the most voted class. This increases the chance that individual classifiers, e.g. Support Vector Machine (SVM), Neural Networks (NN), k-Nearest Neighbor (KNN), and Decision Trees (DT), to show significant discrepancies. Two datasets are used to test the proposed model: CT images containing clinical findings of positive COVID-19 and CT images negative COVID-19. The proposed feature selection algorithm (SFS-Guided WOA) is compared with other optimization algorithms widely used in recent literature to validate its efficiency. The proposed voting classifier (PSO-Guided-WOA) achieved AUC (area under the curve) of 0.995 that is superior to other voting classifiers in terms of performance metrics. Wilcoxon rank-sum, ANOVA, and T-test statistical tests are applied to statistically assess the quality of the proposed algorithms as well.

Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare

Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naive bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.