Low-Code Strategy with Machine Learning for the Healthcare Area: Assessing the Correlation of Occupational Activity with the Incidence of Cancer in Brazil

Artificial intelligence and machine learning are widely utilized and offer significant benefits in various fields of knowledge, including healthcare. However, there is an important barrier to disseminating machine learning among healthcare professionals, which primarily stems from their unfamiliarity with programming and computing concepts. The development strategy known as 'low-code,' when applied to software development, encompasses frameworks and tools that, in short, make application development more accessible to professional communities. The low-code strategy simplifies the software development process. This strategy is particularly relevant for smart cities that seek to develop approaches that enhance the efficiency, humanity, and sustainability of cities, thereby contributing to the achievement of the United Nations' Sustainable Development Goals (SDGs). This article positions the low-code strategy, implemented through the PyCaret framework, as a key element of innovation and contribution to the development of health systems utilizing machine learning in smart cities. The paper presents the low-code strategy through a case study that evaluates the incidence and correlation of occupational activities with the occurrence of cancer in Brazil using an anomaly detection algorithm. The article's contributions include positioning the low-code strategy as an element of innovation in smart cities and presenting a case study that serves as a reference for developing applications with machine learning in the healthcare sector. The case study presented, in turn, presents a differentiated approach to detecting cancer using an anomaly detection algorithm and reiterates correlations between types of cancer and occupational activities.