🤖 AI Summary
This paper addresses the lack of standardized evaluation criteria for machine learning algorithm selection in healthcare, telecommunications, and marketing. We propose a multidimensional, automated model selection framework that jointly optimizes predictive performance—measured by accuracy, precision, and recall—and model complexity, as quantified by the Akaike Information Criterion (AIC). The framework is designed to be domain-agnostic, supporting seamless adaptation across eager, lazy, and hybrid learning paradigms. Evaluated on eight real-world datasets—including cardiovascular disease prediction and fetal health classification—the method consistently identifies optimal models, achieving statistically significant improvements in both predictive accuracy and generalization performance. Crucially, it delivers interpretable and reusable model recommendations tailored to mission-critical applications, thereby bridging the gap between theoretical model selection and practical deployment.
📝 Abstract
The exponential growth of internet generated data has fueled advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL) for extracting actionable insights in marketing,telecom, and health sectors. This chapter explores ML applications across three domains namely healthcare, marketing, and telecommunications, with a primary focus on developing a framework for optimal ML algorithm selection. In healthcare, the framework addresses critical challenges such as cardiovascular disease prediction accounting for 28.1% of global deaths and fetal health classification into healthy or unhealthy states, utilizing three datasets. ML algorithms are categorized into eager, lazy, and hybrid learners, selected based on dataset attributes, performance metrics (accuracy, precision, recall), and Akaike Information Criterion (AIC) scores. For validation, eight datasets from the three sectors are employed in the experiments. The key contribution is a recommendation framework that identifies the best ML model according to input attributes, balancing performance evaluation and model complexity to enhance efficiency and accuracy in diverse real-world applications. This approach bridges gaps in automated model selection, offering practical implications for interdisciplinary ML deployment.