This study explores the efficacy of Automated Machine Learning (AutoML) tools in enhancing linear regression models across industries, focusing on insurance and agriculture. We assessed six widely used AutoML libraries—AutoKeras, AutoGluon, Hyperopt, MLJAR, LightAutoML, and H2O—on a Kaggle-sourced insurance dataset to evaluate their performance in predictive accuracy and operational efficiency. Additionally, we employed TPOT for crop prediction using a Kaggle-based agricultural dataset, demonstrating its application in supporting Sustainable Development Goals (SDGs) related to agriculture. The results showed that AutoML tools vary in effectiveness, with AutoGluon exhibiting superior performance on the insurance dataset due to its low Mean Squared Error (MSE). Similarly, TPOT excelled in optimizing crop yield predictions by automating machine learning pipelines. These findings underscore the versatility of AutoML in handling diverse data contexts, significantly improving model accuracy and reducing manual effort through automation. This study highlights the potential of AutoML to streamline predictive analytics across sectors, enabling data-driven decision-making in industries such as insurance for risk assessment and customer segmentation, as well as in agriculture for sustainable practices and crop optimization. Using datasets from Kaggle reinforces the accessibility and replicability of this research, providing a robust framework for further exploration in diverse industry applications.

Background/Objectives: Cardiovascular diseases (CVDs) represent the foremost public health challenge of recent days. According to the World Health Organization (WHO), nearly 32% of people die from CVDs each year. This paper introduces a quantum-inspired, information-theoretic framework for comparing CVD risk-factor architectures in modeled Indian and global populations. Methods: Each synthetic population is represented as a population state matrix ($\rho$) built from discretized epidemiological variables (age, sex, smoking, diabetes, hypertension, and dyslipidemia). Systemic uncertainty is quantified with von Neumann entropy (equivalent to Shannon entropy for diagonal states), inter-factor dependence with Systemic Mutual Information (SMI), and synthetic population similarity with Uhlmann fidelity. Results: In illustrative modeled scenarios ($N=10,000$ synthetic profiles per synthetic population), diabetes entropy is higher in the Indian comparator ($0.88$ vs. $0.61$ bits), diabetes–hypertension SMI is larger in India (≈0.46 vs. $0.22$ bits), and smoking–hypertension dependence is stronger globally ($0.36$ vs. $0.21$ bits). Overall fidelity is $F\approx 0.76$. Conclusions: These results are hypothesis-generating point estimates and require clinical data verification on harmonized synthetic individual-level data before clinical or policy use. This study represents a quantum information-theoretic framework for comparing modeled CVD risk profiles. Illustrative results suggest stronger diabetes–hypertension dependence in the Indian scenario and stronger smoking–hypertension dependence in the global comparator.