The practice of medical decision making is changing rapidly with the development of innovative computing technologies. The growing interest of data analysis with improvements in big data computer processing methods raises the question of whether machine learning can be integrated with conventional statistics in health research. To help address this knowledge gap, this paper presents a review on the conceptual integration between conventional statistics and machine learning, focusing on the health research. The similarities and differences between the two are compared using mathematical concepts and algorithms. The comparison between conventional statistics and machine learning methods indicates that conventional statistics are the fundamental basis of machine learning, where the black box algorithms are derived from basic mathematics, but are advanced in terms of automated analysis, handling big data and providing interactive visualizations. While the nature of both these methods are different, they are conceptually similar. Based on our review, we conclude that conventional statistics and machine learning are best to be integrated to develop automated data analysis tools. We also strongly believe that machine learning could be explored by health researchers to enhance conventional statistics in decision making for added reliable validation measures.

Integrating data science techniques, including machine learning, natural language processing, and big data analytics, has revolutionized the diagnosis and intervention landscape for Autism Spectrum Disorder (ASD). This rapid review examines these approaches' current applications, benefits, limitations, and ethical considerations while identifying key research gaps and future directions. Data-driven methodologies offer significant advantages, such as enhanced diagnostic accuracy, personalized interventions, and increased accessibility, particularly in resource-limited settings. However, challenges like data quality, algorithmic bias, and interpretability hinder widespread implementation. Additionally, ethical concerns regarding privacy, consent, and equity necessitate careful navigation. Despite these advancements, substantial research gaps remain, including the lack of diverse datasets, limited longitudinal studies, and insufficient generalizability across populations. Future studies must prioritize addressing these gaps by fostering collaboration, ensuring ethical transparency, and developing inclusive, scalable solutions to improve patient outcomes. This review underscores the transformative potential of data science in accelerating ASD care while emphasizing the need for continued innovation and responsible application.

Electric vehicles (EVs) have been progressing rapidly in urban transport systems given their potential in reducing emissions and energy consumptions. The Shared Free-Floating Electric Scooter (SFFES) is an emerging EV publicized to address the first-/last-mile problem in travel. It also offers alternatives for short-distance journeys using cars or ride-hailing services. However, very few SFFES studies have been carried out in developing countries and for university populations. Currently, many universities are facing an increased number of short-distance private car travels on campus. The study is designed to explore the attitudes and perceptions of students and staff towards SFFES usage on campus and the corresponding influencing factors. Three machine learning models were used to predict SFFES usage. Eleven important factors for using SFFESs on campus were identified via the supervised and unsupervised feature selection techniques, with the top three factors being daily travel mode, road features (e.g., green spaces) and age. The random forest model showed the highest accuracy in predicting the usage frequency of SFFESs (93.5%) using the selected 11 variables. A simulation-based optimization analysis was further conducted to discover the characterization of SFFES users, barriers/benefits of using SFFESs and safety concerns.

Breast cancer survival prediction plays a significant role in better understanding the relationship between clinical features and demographic features to i

Workplace accidents are a major concern for occupational safety and health, leading to significant socio-economic impacts due to death and disabilities. Th

This chapter examines how advanced machine learning techniques are transforming environmental, social, and governance (ESG) decision-making in entrepreneurial ventures, enabling data-driven and proactive sustainability strategies. It outlines the ESG imperative in entrepreneurship and presents key machine learning methods for ESG modeling, including the integration of structured and unstructured data sources to capture complex ESG signals. Through case studies such as supply chain risk prediction and greenwashing detection, the chapter demonstrates practical applications of predictive analytics for sustainability. It also reviews the tools and frameworks available for implementing ESG-focused machine learning solutions. Finally, the chapter addresses ethical and operational challenges in deploying AI for ESG, underscoring the need for transparency and fairness. By synthesizing these elements, it highlights the transformative potential of predictive sustainability in advancing sustainable business practices within startups.