It is of paramount importance to enhance medical practices, given how
important it is to protect human life. Medical therapy can be accelerated by
automating patient prediction using machine learning techniques. To double the
efficiency of classifiers, several preprocessing strategies must be adopted for
their crucial duty in this field. Feature selection (FS) is one tool that has
been used frequently to modify data and enhance classification outcomes by
lowering the dimensionality of datasets. Excluded features are those that have
a poor correlation coefficient with the label class, that is, they have no
meaningful correlation with classification and do not indicate where the
instance belongs. Along with the recurring features, which show a strong
association with the remainder of the features. Contrarily, the model being
produced during training is harmed, and the classifier is misled by their
presence. This causes overfitting and increases algorithm complexity and
processing time. These are used in exploration to allow solutions to be found
more thoroughly and in relation to a chosen solution than at random. TLSO,
PLSO, and LLSO stand for Tournament Logarithmic Snake Optimizer, Proportional
Logarithmic Snake Optimizer, and Linear Order Logarithmic Snake Optimizer,
respectively. A number of 22 reference medical datasets were used in
experiments. The findings indicate that, among 86 % of the datasets, TLSO
attained the best accuracy, and among 82 % of the datasets, the best feature
reduction. In terms of the standard deviation, the TLSO also attained
noteworthy reliability and stability. On the basis of running duration, it is,
nonetheless, quite effective.

通过采用机器学习技术自动预测病人情况，提高医疗实践的重要性。特征选择是一种用于修改数据、降低数据集维度、增强分类结果的工具。在相应数据集中，Tournament Logarithmic Snake Optimizer 在实验中达到了最佳准确度和最好的特征降维。

一种以对数生存全局搜索驱动的生物医学蛇优化系统用于特征选择优化及其在疾病识别中的应用

A Bio-Medical Snake Optimizer System Driven by Logarithmic Surviving  Global Search for Optimizing Feature Selection and its application for  Disorder Recognition

Enforcing safety is a key aspect of many problems pertaining to sequential
decision making under uncertainty, which require the decisions made at every
step to be both informative of the optimal decision and also safe. For example,
we value both efficacy and comfort in medical therapy, and efficiency and
safety in robotic control. We consider this problem of optimizing an unknown
utility function with absolute feedback or preference feedback subject to
unknown safety constraints. We develop an efficient safe Bayesian optimization
algorithm, StageOpt, that separates safe region expansion and utility function
maximization into two distinct stages. Compared to existing approaches which
interleave between expansion and optimization, we show that StageOpt is more
efficient and naturally applicable to a broader class of problems. We provide
theoretical guarantees for both the satisfaction of safety constraints as well
as convergence to the optimal utility value. We evaluate StageOpt on both a
variety of synthetic experiments, as well as in clinical practice. We
demonstrate that StageOpt is more effective than existing safe optimization
approaches, and is able to safely and effectively optimize spinal cord
stimulation therapy in our clinical experiments.

本研究针对优化未知效用函数的绝对或偏好反馈时的安全性约束问题，提出了一种有效的安全贝叶斯优化算法 StageOpt，并通过理论和临床实验验证了 StageOpt 在扩展安全区域和最大化效用函数两个阶段上的高效性和效果优于现有方法的特点。