A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome

Applied System Innovation, Journal Year: 2023, Volume and Issue: 6(2), P. 32 - 32

Published: Feb. 23, 2023

Polycystic Ovary Syndrome (PCOS) is a complex disorder predominantly defined by biochemical hyperandrogenism, oligomenorrhea, anovulation, and in some cases, the presence of ovarian microcysts. This endocrinopathy inhibits follicle development causing symptoms like obesity, acne, infertility, hirsutism. Artificial Intelligence (AI) has revolutionized healthcare, contributing remarkably to science engineering domains. Therefore, we have demonstrated an AI approach using heterogeneous Machine Learning (ML) Deep (DL) classifiers predict PCOS among fertile patients. We used Open-source dataset 541 patients from Kerala, India. Among all classifiers, final multi-stack ML models performed best with accuracy, precision, recall, F1-score 98%, 97%, 98%. Explainable (XAI) techniques make model predictions understandable, interpretable, trustworthy. Hence, utilized XAI such as SHAP (SHapley Additive Values), LIME (Local Interpretable Model Explainer), ELI5, Qlattice, feature importance Random Forest for explaining tree-based classifiers. The motivation this study accurately detect while simultaneously proposing automated screening architecture explainable machine learning tools assist medical professionals decision-making.

Language: Английский

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Application of Natural Language Processing and Machine Learning Boosted with Swarm Intelligence for Spam Email Filtering

Mathematics, Journal Year: 2022, Volume and Issue: 10(22), P. 4173 - 4173

Published: Nov. 8, 2022

Spam represents a genuine irritation for email users, since it often disturbs them during their work or free time. Machine learning approaches are commonly utilized as the engine of spam detection solutions, they efficient and usually exhibit high degree classification accuracy. Nevertheless, sometimes happens that good messages labeled and, more often, some emails enter into inbox ones. This manuscript proposes novel approach by combining machine models with an enhanced sine cosine swarm intelligence algorithm to counter deficiencies existing techniques. The introduced was adopted training logistic regression tuning XGBoost part hybrid learning-metaheuristics framework. developed framework has been validated on two public high-dimensional benchmark datasets (CSDMC2010 TurkishEmail), extensive experiments conducted have shown model successfully deals high-degree data. comparative analysis other cutting-edge models, also based metaheuristics, proposed method obtains superior performance in terms accuracy, precision, recall, f1 score, relevant metrics. Additionally, empirically established superiority is using rigid statistical tests.

Language: Английский

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Multi-Swarm Algorithm for Extreme Learning Machine Optimization

Sensors, Journal Year: 2022, Volume and Issue: 22(11), P. 4204 - 4204

Published: May 31, 2022

There are many machine learning approaches available and commonly used today, however, the extreme is appraised as one of fastest and, additionally, relatively efficient models. Its main benefit that it very fast, which makes suitable for integration within products require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to recent literature. Extreme machines still face several challenges need be addressed. The most significant downside performance model heavily depends on allocated weights biases hidden layer. Finding its appropriate values practical tasks represents an NP-hard continuous optimization challenge. Research proposed in this study focuses determining optimal or near layer specific tasks. To address task, a multi-swarm hybrid approach has proposed, based three swarm intelligence meta-heuristics, namely artificial bee colony, firefly algorithm sine-cosine algorithm. method thoroughly validated seven well-known classification benchmark datasets, obtained results compared other already existing similar cutting-edge from simulation point out suggested technique capable obtain better generalization than rest included comparative analysis terms accuracy, precision, recall, f1-score indicators. Moreover, prove combining two algorithms effective joining approaches, additional hybrids generated by pairing, each, methods employed approach, were also implemented against four challenging datasets. findings these experiments superior Sample code devised ELM tuning framework GitHub.

Language: Английский

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A novel firefly algorithm approach for efficient feature selection with COVID-19 dataset

Microprocessors and Microsystems, Journal Year: 2023, Volume and Issue: 98, P. 104778 - 104778

Published: Feb. 6, 2023

Language: Английский

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Improved salp swarm algorithm based on Newton interpolation and cosine opposition-based learning for feature selection

Mathematics and Computers in Simulation, Journal Year: 2024, Volume and Issue: 219, P. 544 - 558

Published: Jan. 2, 2024

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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Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics

Biomolecules, Journal Year: 2025, Volume and Issue: 15(1), P. 81 - 81

Published: Jan. 8, 2025

Cancer's heterogeneity presents significant challenges in accurate diagnosis and effective treatment, including the complexity of identifying tumor subtypes their diverse biological behaviors. This review examines how feature selection techniques address these by improving interpretability performance machine learning (ML) models high-dimensional datasets. Feature methods-such as filter, wrapper, embedded techniques-play a critical role enhancing precision cancer diagnostics relevant biomarkers. The integration multi-omics data ML algorithms facilitates more comprehensive understanding heterogeneity, advancing both personalized therapies. However, such ensuring quality, mitigating overfitting, addressing scalability remain limitations methods. Artificial intelligence (AI)-powered offers promising solutions to issues automating refining extraction process. highlights transformative potential approaches while emphasizing future directions, incorporation deep (DL) integrative strategies for robust reproducible findings.

Language: Английский

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Effective Feature Selection Strategy for Supervised Classification based on an Improved Binary Aquila Optimization Algorithm

Computers & Industrial Engineering, Journal Year: 2023, Volume and Issue: 181, P. 109300 - 109300

Published: May 19, 2023

Language: Английский

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An Optimized Hybrid Approach for Feature Selection Based on Chi-Square and Particle Swarm Optimization Algorithms

Data, Journal Year: 2024, Volume and Issue: 9(2), P. 20 - 20

Published: Jan. 25, 2024

Feature selection is a significant issue in the machine learning process. Most datasets include features that are not needed for problem being studied. These irrelevant reduce both efficiency and accuracy of algorithm. It possible to think about feature as an optimization problem. Swarm intelligence algorithms promising techniques solving this This research paper presents hybrid approach tackling selection. A filter method (chi-square) two wrapper swarm (grey wolf (GWO) particle (PSO)) used different improve system execution time. The performance phases proposed assessed using distinct datasets. results show PSOGWO yields maximum boost 95.3%, while chi2-PSOGWO improvement 95.961% experimental performs better than compared approaches.

Language: Английский

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Symbolic regression as a feature engineering method for machine and deep learning regression tasks

Machine Learning Science and Technology, Journal Year: 2024, Volume and Issue: 5(2), P. 025065 - 025065

Published: May 28, 2024

Abstract In the realm of machine and deep learning (DL) regression tasks, role effective feature engineering (FE) is pivotal in enhancing model performance. Traditional approaches FE often rely on domain expertise to manually design features for (ML) models. context DL models, embedded neural network’s architecture, making it hard interpretation. this study, we propose integrate symbolic (SR) as an process before a ML improve its We show, through extensive experimentation synthetic 21 real-world datasets, that incorporation SR-derived significantly enhances predictive capabilities both models with 34%–86% root mean square error (RMSE) improvement datasets 4%–11.5% datasets. additional realistic use case, show proposed method improves performance predicting superconducting critical temperatures based Eliashberg theory by more than 20% terms RMSE. These results outline potential SR component data-driven improving them interpretability.

Language: Английский

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