A review of machine learning methods to build predictive models for male reproductive health

Developing of artificial intelligence (AI) technology in the medical sector, especially in the part of male reproduction and infertility, is growing rapidly. In both supervised learning and unsupervised learning, AI has been tested and applied to medical personnel to treat their patients. Calculations from simple to complex probability and a combination of some different methods have conducted results of accurate and precise. The results can help determine the condition of male infertility. Artificial neural network (ANN) and fuzzy inference system (FIS) are AI techniques applied to male health issues. ANN is adequate for processing large amounts of combined data in a short time. ANN also has a high level of accuracy and excellent adaptive capabilities. Afterwards, FIS can reflect problems using models with easy to understand, flexible, and also competent to model complex linear functions for decision-making. Based on the advantages of ANN and FIS, it is hoped acquiring prediction results of better and more accurate in male health issues

Male Infertility Prediction Model Using Aritificial Neural Network in Surabaya, Indonesia

In this study, a fertility clinic in Surabaya, Indonesia, uses artificial neural networks (ANNs) to detect male infertility problems. Despite improvements in fertility diagnostics, there are still issues with precisely forecasting infertility from a variety of patient data collected by non-specific entities. In addition to being inconvenient, male fertility diagnostic techniques like semen analysis, sperm function testing, hormone testing, and genetic testing can also cause discomfort and emotional distress for many patients. The research utilizes a dataset of 260 male patients, divided into training (208 samples) and testing (52 samples) sets, to develop predictive models. Employing a backpropagation neural network (BPNN) model, the study achieved a prediction accuracy performance of 96.6%, highlighting the model\u27s effectiveness in identifying abnormalities in semen parameters linked to male infertility. Key parameters influencing predictions included sperm concentration and morphology, with hypospermia emerging as a significant factor. The results demonstrate that BPNNs can enhance diagnostic precision and facilitate tailored treatment plans for patients, addressing the limitations of traditional diagnostic methods. This innovative approach not only contributes to the understanding of male infertility but also emphasizes the importance of integrating advanced technologies in reproductive health diagnostics. The findings suggest that the implementation of predictive models like BPNNs can significantly improve clinical outcomes for couples facing infertility challenges, paving the way for further research and application in this critical area of healthcare

Effect of Electromagnetic-Radiofrequency Devices on Male Infertility at Urban Private Infertility Clinics in East Java

Background:  As technology becomes increasingly integrated into urban lifestyles, the use of mobile phones, laptops/computers, and WiFi has surged, reshaping habits and routines. However, this reliance on devices raises concerns about potential health impacts, including male infertility rates. This study explores how lifestyle factors, particularly mobile phone usage and exposure to electromagnetic radiation (EMR) and radio frequencies (RF), influence male infertility. Methods: Conducted in Surabaya, Indonesia, this study analyzed sperm analysis data from 260 husbands attending private infertility clinics. Data spanning 2018 to 2023 were collected, with interviews conducted to assess EMR-RF exposure. Statistical analysis using the Mann-Whitney test evaluated differences in sperm analysis results between groups exposed to EMR-RF and controls. Results: A notable prevalence of EMR-RF exposure was observed among men aged 31-40, particularly those working indoors. Significant associations were found between EMR-RF exposure and sperm analysis results, with key factors such as mobile phone usage duration, storage practices, and WiFi exposure showing statistically significant correlations (p < 0.05). Conclusion: This study highlights the impact of EMR-RF exposure on male fertility, particularly concerning mobile phone use and WiFi exposure. These findings underscore the importance of further research and potential interventions to mitigate the health risks associated with EMR-RF exposure in urban populations

Open Collaboration Platform

Open science can accelerate the pace of research and contribute to a more equitable society. However, the current culture of scientific research is not optimally structured to promote extensive sharing of a range of outputs. In this policy position paper, we outline current open science practices and key bottlenecks in their broader adoption. We propose that national science agencies create a digital infrastructure framework that would standardize open science principles and make them actionable. We also suggest ways of redefining research success to align better with open science, and to incentivize a system where sharing various research outputs is beneficial to researchers

Reducing barriers to open science by standardizing practices and realigning incentives

Open science can accelerate the pace of research and contribute to a more equitable society. However, the current culture of scientific research is not optimally structured to promote extensive sharing of a range of outputs. In this policy position paper, we outline current open science practices and key bottlenecks in their broader adoption. We propose that national science agencies create a digital infrastructure framework that would standardize open science principles and make them actionable. We also suggest ways of redefining research success to align better with open science, and to incentivize a system where sharing various research outputs is beneficial to researchers

BIOS 259: The Art of Reproducible Science – A Hands-on Approach

<p>This mini-course is designed to equip graduate students and postdocs with essential skills to ensure computational research reproducibility. Through practical exercises and interactive sessions, participants will learn best practices, tools, and techniques for doing open and reproducible research. Topics covered include version control, containerization, data management, workflows, and documentation strategies. This course empowers students to overcome challenges associated with reproducibility, fostering rigorous scientific inquiry, and enhancing the credibility and impact of their computational work, while also exploring the primary causes and consequences of irreproducibility in research. Participants will gain valuable insights and practical experience in achieving computational reproducibility across various domains, including biology.</p> <p><strong>Note: This is a beta release of the course BIOS259.</strong></p&gt

bgsimon11/bios259-w24: BIOS259_W24_v1.0.0

<p>Release</p&gt

Open Collaboration Platform

Open science can accelerate the pace of research and contribute to a more equitable society. However, the current culture of scientific research is not optimally structured to promote extensive sharing of a range of outputs. In this policy position paper, we outline current open science practices and key bottlenecks in their broader adoption. We propose that national science agencies create a digital infrastructure framework that would standardize open science principles and make them actionable. We also suggest ways of redefining research success to align better with open science, and to incentivize a system where sharing various research outputs is beneficial to researchers