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.

通过比较分析，本研究证明了生物启发式优化算法在慢性疾病预测的特征选择中的有效性，并强调了数据预处理的重要性，为提高医疗保健结果提供了宝贵的见解。

基于生物启发特征选择的慢性疾病预测数据集优化

Dataset Optimization for Chronic Disease Prediction with Bio-Inspired  Feature Selection

Positive-Unlabeled (PU) Learning is a challenge presented by binary
classification problems where there is an abundance of unlabeled data along
with a small number of positive data instances, which can be used to address
chronic disease screening problem. State-of-the-art PU learning methods have
resulted in the development of various risk estimators, yet they neglect the
differences among distinct populations. To address this issue, we present a
novel Positive-Unlabeled Learning Tree (PUtree) algorithm. PUtree is designed
to take into account communities such as different age or income brackets, in
tasks of chronic disease prediction. We propose a novel approach for binary
decision-making, which hierarchically builds community-based PU models and then
aggregates their deliverables. Our method can explicate each PU model on the
tree for the optimized non-leaf PU node splitting. Furthermore, a mask-recovery
data augmentation strategy enables sufficient training of the model in
individual communities. Additionally, the proposed approach includes an
adversarial PU risk estimator to capture hierarchical PU-relationships, and a
model fusion network that integrates data from each tree path, resulting in
robust binary classification results. We demonstrate the superior performance
of PUtree as well as its variants on two benchmarks and a new
diabetes-prediction dataset.

提出了一种新颖的 Positive-Unlabeled Learning Tree (PUtree) 算法，该算法通过考虑不同群体（如年龄或收入段）来解决慢性疾病预测任务中的正无标记学习问题，包括建立基于群体的 PU 模型、采用掩蔽恢复数据增强策略进行模型训练、使用对抗 PU 风险评估器捕捉层次化的 PU 关系，并通过模型融合网络对每条路径的数据进行整合，实现稳健的二元分类结果。在两个基准数据集和一个新的糖尿病预测数据集上验证了 PUtree 及其变体的卓越性能。