While type 2 diabetes is predominantly found in the elderly population, recent publications indicate an increasing prevalence in the young adult population. Failing to diagnose it in the minority younger age group could have significant adverse effects on their health. Several previous works acknowledge the bias of machine learning models towards different gender and race groups and propose various approaches to mitigate it. However, those works failed to propose any effective methodologies to diagnose diabetes in the young population, which is the minority group in the diabetic population. This is the first paper where we mention digital ageism towards the young adult population diagnosing diabetes. In this paper, we identify this deficiency in traditional machine learning models and propose an algorithm to mitigate the bias towards the young population when predicting diabetes. Deviating from the traditional concept of one-model-fits-all, we train customized machine-learning models for each age group. Our pipeline trains a separate machine learning model for every 5-year age band (i.e., age groups 30-34, 35-39, and 40-44). The proposed solution consistently improves recall of diabetes class by 26% to 40% in the young age group (30-44). Moreover, our technique outperforms 7 commonly used whole-group resampling techniques (i.e., random oversampling, random undersampling, SMOTE, ADASYN, Tomek-links, ENN, and Near Miss) by at least 36% in terms of diabetes recall in the young age group. Feature important analysis shows that the age attribute has a significant contribution to the decision of the original model, which was marginalized in the age-personalized model. Our method shows improved performance (e.g., balanced accuracy improved 7-12%) over multiple machine learning models and multiple sampling algorithms.

Extensive research has been conducted to explore cryptographic API misuse in Java. However, despite the tremendous popularity of the Python language, uncovering similar issues has not been fully explored. The current static code analysis tools for Python are unable to scan the increasing complexity of the source code. This limitation decreases the analysis depth, resulting in more undetected cryptographic misuses. In this research, we propose Cryptolation, a Static Code Analysis (SCA) tool that provides security guarantees for complex Python cryptographic code. Most existing analysis tools for Python solely focus on specific Frameworks such as Django or Flask. However, using a SCA approach, Cryptolation focuses on the language and not any framework. Cryptolation performs an inter-procedural data-flow analysis to handle many Python language features through variable inference (statically predicting what the variable value is) and SCA. Cryptolation covers 59 Python cryptographic modules and can identify 18 potential cryptographic misuses that involve complex language features. In this paper, we also provide a comprehensive analysis and a state-of-the-art benchmark for understanding the Python cryptographic Application Program Interface (API) misuses and their detection. Our state-of-the-art benchmark PyCryptoBench includes 1,836 Python cryptographic test cases that covers both 18 cryptographic rules and five language features. PyCryptoBench also provides a framework for evaluating and comparing different cryptographic scanners for Python. To evaluate the performance of our proposed cryptographic Python scanner, we evaluated Cryptolation against three other state-of-the-art tools: Bandit, Semgrep, and Dlint. We evaluated these four tools using our benchmark PyCryptoBench and manual evaluation of (four Top-Ranked and 939 Un-Ranked) real-world projects. Our results reveal that, overall, Cryptolation achieved the highest precision throughout our testing; and the highest accuracy on our benchmark. Cryptolation had 100% precision on PyCryptoBench, and the highest precision on the real-world projects.