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Prediction and interpretation of fetal health status in the womb with machine learning method using tocogram data
This study utilized the publicly available fetal health dataset containing cardiotocogram data to evaluate the performance of various machine learning algorithms for predicting the status of fetal health. The algorithms were implemented with Python and the PyCaret library, and Synthetic Minority Over-sampling Technique (called SMOTE) used for countering the imbalanced distribution of the target variable. Notably, the Light Gradient Boosting Machine exhibited the highest accuracy and F1 score, as well as the lowest severity of misclassifications according to our penalty score system that considered the differing severities of misclassification errors. The study demonstrated the potential of machine learning algorithms to accurately predict fetal health and enhance clinical decision making processes. Validation of the models on more diverse and larger datasets is recommended. The study also showcased the utility of user friendly libraries like PyCaret, highlighting how clinics could potentially build their own machine learning models with lesser effort.
Predicting potential resignations in the companies using machine learning techniques
The ongoing digitalization process has ushered in a transformative era for businesses, offering numerous advantages. It has streamlined operations, enhanced connectivity, and facilitated unprecedented data-driven decision-making. However, it also brings challenges, with a central concern being the shifting dynamics of the labor market and evolving employee demands. Organizational priorities now revolve around identifying and retaining skilled employees. In an era where a company's value is closely tied to its workforce's skills, knowledge, and innovation, attracting and retaining talent is not just a preference but a strategic imperative. Success hinges on acquiring and retaining top talent. To tackle this challenge, organizations are adopting sophisticated tools and metrics, with Key Performance Indicators (KPIs) becoming essential for gauging employee engagement. These metrics serve as barometers for an organization's workforce health. The true test lies in proactively using these metrics to identify potential resignations and develop effective retention strategies. This abstract explores strategies for recognizing early signs of employee attrition and crafting retention tactics. Leveraging advanced technologies such as Support Vector Machines, Artificial Neural Networks, and other machine learning algorithms, the study aims to provide deeper insights into factors driving resignations. By harnessing data analytics and machine learning, organizations can tailor retention efforts accordingly. In summary, this research comprehensively explores the challenges of digitalization on the workforce and how organizations can respond. Emphasizing proactive, data-driven approaches, the study offers valuable insights for businesses navigating talent retention in the digital age.