Dataset Optimization for Chronic Disease Prediction with Bio-Inspired
Feature Selection
- URL: http://arxiv.org/abs/2401.05380v1
- Date: Sun, 17 Dec 2023 18:18:34 GMT
- Title: Dataset Optimization for Chronic Disease Prediction with Bio-Inspired
Feature Selection
- Authors: Abeer Dyoub, Ivan Letteri
- Abstract summary: The study contributes to the advancement of predictive analytics in the realm of chronic diseases.
The potential impact of this work extends to early intervention, precision medicine, and improved patient outcomes.
- Score: 0.32634122554913997
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In this study, we investigated the application of bio-inspired optimization
algorithms, including Genetic Algorithm, Particle Swarm Optimization, and Whale
Optimization Algorithm, for feature selection in chronic disease prediction.
The primary goal was to enhance the predictive accuracy of models streamline
data dimensionality, and make predictions more interpretable and actionable.
The research encompassed a comparative analysis of the three bio-inspired
feature selection approaches across diverse chronic diseases, including
diabetes, cancer, kidney, and cardiovascular diseases. Performance metrics such
as accuracy, precision, recall, and f1 score are used to assess the
effectiveness of the algorithms in reducing the number of features needed for
accurate classification.
The results in general demonstrate that the bio-inspired optimization
algorithms are effective in reducing the number of features required for
accurate classification. However, there have been variations in the performance
of the algorithms on different datasets.
The study highlights the importance of data pre-processing and cleaning in
ensuring the reliability and effectiveness of the analysis.
This study contributes to the advancement of predictive analytics in the
realm of chronic diseases. The potential impact of this work extends to early
intervention, precision medicine, and improved patient outcomes, providing new
avenues for the delivery of healthcare services tailored to individual needs.
The findings underscore the potential benefits of using bio-inspired
optimization algorithms for feature selection in chronic disease prediction,
offering valuable insights for improving healthcare outcomes.
Related papers
- Comparative Study of Machine Learning Algorithms in Detecting Cardiovascular Diseases [0.0]
The detection of cardiovascular diseases (CVD) using machine learning techniques represents a significant advancement in medical diagnostics.
This study explores a comparative analysis of various machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost.
The findings highlight the efficacy of ensemble methods and advanced algorithms in providing reliable predictions, thereby offering a comprehensive framework for CVD detection.
arXiv Detail & Related papers (2024-05-27T11:29:54Z) - Application of Machine Learning Algorithms in Classifying Postoperative Success in Metabolic Bariatric Surgery: A Comprehensive Study [0.32985979395737786]
This study presents a novel machine learning approach to classify patients in the context of metabolic bariatric surgery.
Various machine learning models, including GaussianNB, ComplementNB, KNN, Decision Tree, KNN with RandomOverSampler, and KNN with SMOTE, were applied to a dataset of 73 patients.
arXiv Detail & Related papers (2024-03-29T11:27:37Z) - A novel Network Science Algorithm for Improving Triage of Patients [2.209921757303168]
Patient triage plays a crucial role in healthcare, ensuring timely and appropriate care based on the urgency of patient conditions.
Recent interest has been in leveraging artificial intelligence (AI) to develop algorithms for triaging patients.
This paper presents the development of a novel algorithm for triaging patients. It is based on the analysis of patient data to produce decisions regarding their prioritization.
arXiv Detail & Related papers (2023-10-09T08:47:12Z) - A New Deep Learning and XAI-Based Algorithm for Features Selection in
Genomics [5.787117733071415]
The paper proposes a novel algorithm to perform Feature Selection on genomic-scale data.
Results of the application on a Chronic Lymphocytic Leukemia dataset evidence the effectiveness of the algorithm.
arXiv Detail & Related papers (2023-03-29T16:44:13Z) - Benchmarking Heterogeneous Treatment Effect Models through the Lens of
Interpretability [82.29775890542967]
Estimating personalized effects of treatments is a complex, yet pervasive problem.
Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools.
We use post-hoc feature importance methods to identify features that influence the model's predictions.
arXiv Detail & Related papers (2022-06-16T17:59:05Z) - Learning Predictions for Algorithms with Predictions [49.341241064279714]
We introduce a general design approach for algorithms that learn predictors.
We apply techniques from online learning to learn against adversarial instances, tune robustness-consistency trade-offs, and obtain new statistical guarantees.
We demonstrate the effectiveness of our approach at deriving learning algorithms by analyzing methods for bipartite matching, page migration, ski-rental, and job scheduling.
arXiv Detail & Related papers (2022-02-18T17:25:43Z) - Compactness Score: A Fast Filter Method for Unsupervised Feature
Selection [66.84571085643928]
We propose a fast unsupervised feature selection method, named as, Compactness Score (CSUFS) to select desired features.
Our proposed algorithm seems to be more accurate and efficient compared with existing algorithms.
arXiv Detail & Related papers (2022-01-31T13:01:37Z) - Resource Planning for Hospitals Under Special Consideration of the
COVID-19 Pandemic: Optimization and Sensitivity Analysis [87.31348761201716]
Crises like the COVID-19 pandemic pose a serious challenge to health-care institutions.
BaBSim.Hospital is a tool for capacity planning based on discrete event simulation.
We aim to investigate and optimize these parameters to improve BaBSim.Hospital.
arXiv Detail & Related papers (2021-05-16T12:38:35Z) - Increasing the efficiency of randomized trial estimates via linear
adjustment for a prognostic score [59.75318183140857]
Estimating causal effects from randomized experiments is central to clinical research.
Most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control.
arXiv Detail & Related papers (2020-12-17T21:10:10Z) - Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic
Programmed Deep Kernels [93.58854458951431]
We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases.
Our analysis considers a spectrum of neural and symbolic machine learning approaches.
We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
arXiv Detail & Related papers (2020-09-16T15:16:03Z) - Surrogate-assisted performance prediction for data-driven knowledge
discovery algorithms: application to evolutionary modeling of clinical
pathways [0.0]
The paper proposes and investigates an approach for surrogate-assisted performance prediction of data-driven knowledge discovery algorithms.
The approach is based on the identification of surrogate models for prediction of the target algorithm's quality and performance.
arXiv Detail & Related papers (2020-04-02T16:49:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.