在荷尔蒙失衡数据集上使用模糊机器学习逻辑
Rabia Khushal1, Ubaida Fatima1
1Department of Mathematics, NED University of Engineering & Technology, Pakistan.
Computers in biology and medicine
|April 17, 2024
概括
一种新的模糊数据转换技术通过将二进制数据转换为三类系统来增强PCOS诊断. 这种改进的方法为检测多囊卵巢综合征 (PCOS) 提供了更广泛的范围,并使早期预防措施成为可能.
科学领域:
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 荷尔蒙失衡,特别是多囊性卵巢综合征 (PCOS),显著影响女性的健康.
- 现有的PCOS数据集往往具有局限性,包括二进制变量和有限的诊断输出.
- 这限制了准确诊断和管理PCOS的能力.
研究的目的:
- 为激素失衡数据集引入一种新的模糊数据转换技术.
- 提高多囊卵巢综合征 (PCOS) 的诊断能力.
- 为了提高分类准确度,并为PCOS提供更广泛的诊断谱.
主要方法:
- 开发了一种新的模糊数据转换技术,并应用于激素失衡数据集.
- 具有二进制响应的输入变量被转换为模糊格式.
- 实现了一个自适应模糊机器学习逻辑模型,用于对转换的数据集进行推理.
主要成果:
- 模糊转换将诊断频谱从二进制 (存在/缺席) 扩展到三个类,表明潜在的PCOS存在.
- 机器学习应用于模糊转换数据集,与未转换数据集相比,提供了更细致的诊断.
- 这种更广泛的频谱允许更早的检测,并提醒患者采取预防措施.
结论:
- 拟议的模糊数据转换技术有效地解决了PCOS诊断二进制数据集的局限性.
- 这种方法提高了机器学习模型的性能,为PCOS风险提供了更全面的理解.
- 通过模糊转换的早期检测可以导致及时干预和改善患者的结果.
相关概念视频
Feedback Loops
57.5K
In most cases, excessive hormone production is prevented by negative feedback—a loop that starts with a stimulus inducing the release of a particular substance, like a hormone, to maintain a certain level before triggering a signal that results in a decrease in further release of the hormone.
57.5K
Hormonal Regulation of the Menstrual Cycle
347
The ovarian cycle regulates endometrial changes throughout a single menstrual cycle via the coordinated action of gonadotrophin-releasing hormone (GnRH) and gonadotrophins.
At puberty, GnRH begins a pulsatile release pattern, which triggers the anterior pituitary gland to secrete follicle-stimulating hormone (FSH) and luteinizing hormone (LH). The frequency and amplitude of GnRH pulses vary across the menstrual cycle, with faster pulses favoring LH release and slower pulses favoring FSH...
At puberty, GnRH begins a pulsatile release pattern, which triggers the anterior pituitary gland to secrete follicle-stimulating hormone (FSH) and luteinizing hormone (LH). The frequency and amplitude of GnRH pulses vary across the menstrual cycle, with faster pulses favoring LH release and slower pulses favoring FSH...
347
Hormonal Control of the Ovarian Cycle
476
The ovarian cycle is meticulously regulated by the hypothalamic-pituitary-gonadal axis. This cycle orchestrates the release of a mature oocyte, essential for reproduction.
Before puberty, the hypothalamus releases GnRH in a low frequency, low amplitude pulsatile manner. This along with the immature hypothalamic-pituitary-gonadal axis activity, results in low estrogen levels and the absence of a fully functional ovarian cycle. At puberty, GnRH secretion increases in both frequency and...
Before puberty, the hypothalamus releases GnRH in a low frequency, low amplitude pulsatile manner. This along with the immature hypothalamic-pituitary-gonadal axis activity, results in low estrogen levels and the absence of a fully functional ovarian cycle. At puberty, GnRH secretion increases in both frequency and...
476
Regulation of Hormone Secretion
3.4K
Regulation of hormone secretion is a finely tuned orchestration driven by various types of stimuli, encompassing neural, humoral, and hormonal signals. Environmental cues instigate neural stimuli, where action potentials traverse nerve fibers to reach their designated targets. An illustrative scenario is the body's response to stress, wherein the sympathetic nervous system releases epinephrine from the adrenal glands, inducing the well-known 'fight or flight' reaction.
Humoral...
Humoral...
3.4K
Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis
39
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
39


