Thyroid disease treatment prediction with machine learning approaches

The thyroid is an endocrine gland located in the anterior region of the neck: its main task is to produce thyroid hormones, which are functional to our entire body. Its possible dysfunction can lead to the production of an insufficient or excessive amount of thyroid hormone. Therefore, the thyroid can become inflamed or swollen due to one or more swellings forming inside it. Some of these nodules can be the site of malignant tumors. One of the most used treatments is sodium levothyroxine, also known as LT4, a synthetic thyroid hormone used in the treatment of thyroid disorders and diseases. Predictions about the treatment can be important for supporting endocrinologists' activities and improve the quality of the patients' life. To date, there are numerous studies in the literature that focus on the prediction of thyroid diseases on the trend of the hormonal parameters of people. This work, differently, aims to predict the LT4 treatment trend for patients suffering from hypothyroidism. To this end, a dedicated dataset was built that includes medical information related to patients being treated in the”AOU Federico II” hospital of Naples. For each patient, the clinical history is available over time, and therefore on the basis of the trend of the hormonal parameters and other attributes considered it was possible to predict the course of each patient's treatment in order to understand if this should be increased or decreased. To conduct this study, we used different machine learning algorithms. In particular, we compared the results of 10 different classifiers. The performances of the different algorithms show good results, especially in the case of the Extra-Tree Classifier, where the accuracy reaches 84%

Model Checking to Improve Precision of Design Pattern Instances Identification in OO Systems

In the last two decades some methods and tools have been proposed to identify the Design Pattern (DP) instances implemented in an existing Object Oriented (OO) software system. This allows to know which OO components are involved in each DP instance. Such a knowledge is useful to better understand the system thus reducing the effort to modify and evolve it. The results obtained by the existing methods and tools can suffer a lack of completeness or precision due to the presence of false positive/negative. Model Checking (MC) algorithms can be used to improve the precision of DP's instances detected by a tool by automatically refining the results it produces. In this paper a MC based technique is defined and applied to the results of an existing DPs mining tool, called Design Pattern Finder (DPF), to improve the precision by verifying automatically the DPs instances it detects. To verify and assess the feasibility and the effectiveness of the proposed technique, we carried out a case study where it was applied on some open source OO systems. The results showed that the proposed technique allowed to improve the precision of the DPs instances detected by the DPF tool

Distributed Software Development with Knowledge Experience Packages

In software production process, a lot of knowledge is created and remain silent. Therefore, it cannot be reused to improve the effectiveness and the efficiency of these processes. This problem is amplified in the case of a distributed production. In fact, distributed software development requires complex context specific knowledge regarding the particularities of different technologies, the potential of existing software, the needs and expectations of the users. This knowledge, which is gained during the project execution, is usually tacit and is completely lost by the company when the production is completed. Moreover, each time a new production unit is hired, despite the diversity of culture and capacity of people, it is necessary to standardize the working skills and methods of the different teams if the company wants to keep the quality level of processes and products. In this context, we used the concept of Knowledge Experience Package (KEP), already specified in previous works and the tool realized to support KEP approach. In this work, we have carried out an experiment in an industrial context in which we compared the software development supported by KEPs with the development achieved without it

Towards automatic assessment of object-oriented programs

The computing education community has shown a long-time interest in how to analyze the Object-Oriented (OO) source codedeveloped by students to provide them with useful formative tips.In this paper, we propose and evaluate an approach to analyzehow students use Java and its language constructs. The approach isimplemented through a cloud-based integrated development environment (IDE) and it is based on the analysis of the most commonviolations of the OO paradigm in the student source code. Moreover,the IDE supports the automatic generation of reports about student's mistakes and misconceptions that can be used by instructorsto improve the course design. The paper discusses the preliminaryresults of an experiment performed in a class of a Programming IIcourse to investigate the effects of the provided reports in terms ofcoding ability (concerning the correctness of the produced code)