Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Dec. 18, 2023

Abstract The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. Cleveland Heart disease dataset with sixteen techniques three categories filter, wrapper, and evolutionary were used. Then seven Bayes net, Naïve (BN), multivariate linear model (MLM), Support Vector Machine (SVM), logit boost, j48, Random Forest applied to identify best models prediction. Precision, F-measure, Specificity, Accuracy, Sensitivity, ROC area, PRC measured compare methods' effect on prediction algorithms. results demonstrate that resulted significant improvements performance some (e.g., j48), whereas it led a decrease other (e.g. MLP, RF). SVM-based filtering have best-fit accuracy 85.5. In fact, best-case scenario, result + 2.3 accuracy. SVM-CFS/information gain/Symmetrical uncertainty highest improvement this index. filter number features selected outperformed terms models' ACC, F-measures. However, wrapper-based improved from sensitivity specificity points view.

Language: Английский

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Gated Deep Reinforcement Learning With Red Deer Optimization for Medical Image Classification

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 58982 - 58993

Published: Jan. 1, 2023

One of the most complex areas image processing is classification, which heavily relied upon in clinical care and educational activities. However, conventional models have reached their limits effectiveness require extensive time effort to extract choose classification variables. In addition, large volume medical data being produced makes manual procedures ineffective prone errors. Deep learning has shown promise for many problems. this study, a deep learning-based model developed decrease misclassifications handle amounts data. The Adaptive Guided Bilateral Filter used filter images, texture edge attributes are gathered using Spectral Gabor Wavelet Transform. Black Widow Optimization method best features, then input into Red Deer Optimization-enhanced Gated Reinforcement Learning network classification. brain tumor MRI dataset was test on MATLAB platform, results showed an accuracy 98.8%.

Language: Английский

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Enhancing Feature Selection Through Metaheuristic Hybrid Cuckoo Search and Harris Hawks Optimization for Cancer Classification

Published: March 29, 2024

Gene expression platforms offer vast amounts of data that can be utilized for investigating diverse biological processes. However, due to the existence redundant and irrelevant genes, it remains challenging identify crucial genes from high-dimensional data. To overcome this obstacle, researchers have introduced different feature selection (FS) methods. Developing more efficient accurate FS techniques is essential select important classification complex information with multiple dimensions many purposes. tackle difficulty selecting in datasets, a novel approach called Harris hawks optimization cuckoo search algorithm (HHOCSA) proposed commonly used machine learning classifiers such as K-nearest neighbors (KNN), support vector (SVM), naive Bayes (NB). The effectiveness hybrid gene was assessed using six datasets compared other features. experimental findings demonstrate HHOCSA outperforms alternative methods when considering performance metrics accuracy measures precision, sensitivity, specificity. Furthermore, study both computationally consistent terms variability Therefore, useful instrument cancer help medical professionals make better-informed decisions diagnosis.

Language: Английский

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Improved Binary Meerkat Optimization Algorithm for efficient feature selection of supervised learning classification

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 292, P. 111616 - 111616

Published: March 7, 2024

Language: Английский

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Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 82199 - 82207

Published: Jan. 1, 2023

An IoT healthcare system refers to the use of Internet Things (IoT) devices and technologies in industry. It involves integration various interconnected devices, sensors, systems collect, monitor, transmit health-related data for medical purposes. Blockchain-assisted intrusion detection on is an innovative approach enhancing security privacy sensitive data. By combining decentralized immutable nature blockchain technology with (IDS), it possible create a more robust trustworthy framework systems. With this motivation, study presents Blockchain Assisted Healthcare System using Ant Lion Optimizer Hybrid Deep Learning (BHS-ALOHDL) technique. The presented BHS-ALOHDL technique enables sector securely detects intrusions system. To accomplish this, performs ALO based feature subset selection (ALO-FSS) produce series vectors. HDL model integrates convolutional neural network (CNN) features long short-term memory (LSTM) detection. Lastly, flower pollination algorithm (FPA) exploited optimal hyperparameter tuning approach, which results enhanced rate. experimental outcome was tested two benchmark datasets outcomes indicate promising performance over other models.

Language: Английский

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Decoding China’s new-type industrialization: Insights from an XGBoost-SHAP analysis

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 478, P. 143927 - 143927

Published: Oct. 13, 2024

Language: Английский

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A Deep Learning Approach for Kidney Disease Recognition and Prediction through Image Processing

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(6), P. 3621 - 3621

Published: March 12, 2023

Chronic kidney disease (CKD) is a gradual decline in renal function that can lead to damage or failure. As the progresses, it becomes harder diagnose. Using routine doctor consultation data evaluate various stages of CKD could aid early detection and prompt intervention. To this end, researchers propose strategy for categorizing using an optimization technique inspired by learning process. Artificial intelligence has potential make many things world seem possible, even causing surprise with its capabilities. Some doctors are looking forward advancements technology scan patient’s body analyse their diseases. In regard, advanced machine algorithms have been developed detect presence disease. This research presents novel deep model, which combines fuzzy neural network, recognition prediction The results show proposed model accuracy 99.23%, better than existing methods. Furthermore, detecting chronic be confirmed without involvement as future work. Compared information mining classifications, approach shows improved classification, precision, F-measure, sensitivity metrics.

Language: Английский

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A Comprehensive Review of Metaheuristics for Hyperparameter Optimization in Machine Learning

Published: March 29, 2024

Hyperparameter optimization is a critical step in the development and fine-tuning of machine learning (ML) models. Metaheuristic techniques have gained significant popularity for addressing this challenge due to their ability search hyperparameter space efficiently. In review, we present detailed analysis various metaheuristic ML, encompassing population-based, single solution-based, hybrid approaches. We explore application metaheuristics Bayesian neural architecture search, two prominent areas within field. Moreover, provide comparative these based on established criteria evaluate performance diverse ML applications. Finally, discuss future directions open challenges with special emphasis opportunities improvement metaheuristics. Other crucial issues like adaptability new paradigms, computational complexity, scalability are also discussed critically. This review aims researchers practitioners comprehensive understanding state-of-the-art tuning, thereby facilitating informed decisions advancements

Language: Английский

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BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images

Diagnostics, Journal Year: 2023, Volume and Issue: 13(9), P. 1618 - 1618

Published: May 3, 2023

The early detection of breast cancer using mammogram images is critical for lowering women’s mortality rates and allowing proper treatment. Deep learning techniques are commonly used feature extraction have demonstrated significant performance in the literature. However, these features do not perform well several cases due to redundant irrelevant information. We created a new framework diagnosing entropy-controlled deep flower pollination optimization from images. In proposed framework, filter fusion-based method contrast enhancement developed. pre-trained ResNet-50 model then improved trained transfer on both original enhanced datasets. extracted combined into single vector following phase serial technique known as mid-value features. top classified neural networks machine classifiers stage. To accomplish this, with entropy control has been exercise three publicly available datasets: CBIS-DDSM, INbreast, MIAS. On selected datasets, achieved 93.8, 99.5, 99.8% accuracy, respectively. Compared current methods, increase accuracy decrease computational time explained.

Language: Английский

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Metaheuristic Algorithms and Their Applications in Different Fields

Published: March 29, 2024

A potent method for resolving challenging optimization issues is provided by metaheuristic algorithms, which are heuristic approaches. They provide an effective technique to explore huge solution spaces and identify close ideal or optimal solutions. iterative often inspired natural social processes. This study provides comprehensive information on algorithms the many areas in they used. Heuristic well-known their success handling issues. a tool problem-solving. Twenty such as tabu search, particle swarm optimization, ant colony genetic simulated annealing, harmony included article. The article extensively explores applications of these diverse domains engineering, finance, logistics, computer science. It underscores particular instances where have found utility, optimizing structural design, controlling dynamic systems, enhancing manufacturing processes, managing supply chains, addressing problems artificial intelligence, data mining, software engineering. paper thorough insight into versatile deployment across different sectors, highlighting capacity tackle complex wide range real-world scenarios.

Language: Английский

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