Increasing the sustainability of a system can be achieved by evaluating the system, identifying the issues and their root cause and solving them. Performance evaluation translates into key performance indicators (KPIs) with a high impact on increasing overall efficacy and efficiency. As the pool of KPIs has increased over time in the context of evaluating the supply chain management (SCM) system’s performance and assessing, communicating and managing its risks, a mathematical model based on neural networks has been developed. The SCM system has been structured into subsystems with the most relevant KPIs for set subsystems and their most important contributions on the increase in the overall SCM system performance and sustainability. As a result of the performed research based on the interview method, the five most relevant KPIs of each SCM subsystem and the most relevant problems are underlined. The main goal of this paper is to develop a performance evaluation model that links specific problems with the most relevant KPIs for every subsystem of the supply chain management. This paper demonstrates that by using data mining, the relationship between certain problems that appear in the supply chain management of every company and specific KPIs can be identified. The paper concludes with a graphical user interface (GUI) based on neural networks using the multilayer perceptron artificial intelligence algorithm where the most trustworthy KPIs for each selected problem can be predicted. This aspect provides a highly innovative contribution in solving supply chain management problems provided by organizations by allowing them to holistically track, communicate, analyze and improve the SCM system and ensure overall system sustainability.

Machine learning offers significant potential for lung cancer detection, enabling early diagnosis and potentially improving patient outcomes. Feature extraction remains a crucial challenge in this domain. Combining the most relevant features can further enhance detection accuracy. This study employed a hybrid feature extraction approach, which integrates both Gray-level co-occurrence matrix (GLCM) with Haralick and autoencoder features with an autoencoder. These features were subsequently fed into supervised machine learning methods. Support Vector Machine (SVM) Radial Base Function (RBF) and SVM Gaussian achieved perfect performance measures, while SVM polynomial produced an accuracy of 99.89% when utilizing GLCM with an autoencoder, Haralick, and autoencoder features. SVM Gaussian achieved an accuracy of 99.56%, while SVM RBF achieved an accuracy of 99.35% when utilizing GLCM with Haralick features. These results demonstrate the potential of the proposed approach for developing improved diagnostic and prognostic lung cancer treatment planning and decision-making systems.