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Breast cancer is mostly a female disease, but it may affect men as well even at a considerably lower percentage. An automated diagnosis system should be built for early detection because manual breast cancer diagnosis takes a long time. Doctors have lately achieved significant advances in the early identification and treatment of breast cancer in order to decrease the rate of mortality caused by the latter. Researchers, on the other hand, are analysing large amounts of complicated medical data by employing a combination of statistical and machine learning methodologies to assist clinicians in predicting breast cancer. Various machine learning approaches, including ontology-based Machine Learning methods, have lately played an essential role in medical science by building an automated system that can identify breast cancer. This study examines and evaluates the most popular machine learning algorithms, besides the ontological model based on Machine Learning. Among the classification methods investigated were Naive Bayes, Decision Tree, Logistic Regression, Support Vector Machine, Artificial Neural Network, Random Forest, and k-Nearest Neighbours. The dataset utilized has 683 instances and is available for download from the Kaggle website. The findings are assessed using performance measures generated from the confusion matrix, such as F-Measure, Accuracy, Precision, and Recall. The ontology model surpassed all machine learning techniques, according to the results.

Nowadays, cardiovascular diseases (CVD) are one of the most critical reasons for death. Thus, CVD prediction is a crucial challenge in the field of clinical data analysis. Researchers are using a variety of statistical and machine learning methods to assess immense amounts of complex medical data, to help doctors predict heart disease. In this paper, we proposed a new approach to predict CVD using ML techniques and Ontology to build an efficient ontology-based model able to predict accurately the presence of cardiac disease and establish an early diagnosis. the approach consists of extracting rules from the Decision Tree algorithm that differentiate the patients with or without cardiovascular disease then implementing these rules in the ontology reasoner using Semantic Web Rule Language (SWRL). The ontology model result reach high classification accuracy of 75% compared to the decision tree model. The approach can be employed in the medical field for the prediction of cardiovascular diseases.

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Abstract. The new era of the Web is known as the semantic Web or the Web of data. The semantic Web depends on ontologies that are seen as one of its pillars. The bigger these ontologies, the greater their exploitation. However, when these ontologies become too big other problems may appear, such as the complexity to charge big files in memory, the time it needs to download such files and especially the time it needs to make reasoning on them. We discuss in this paper approaches for segmenting such big Web ontologies as well as its usefulness. The segmentation method extracts from an existing ontology a segment that represents a layer or a generation in the existing ontology; i.e. a horizontally extraction. The extracted segment should be itself an ontology.Keywords: Ontology; Segmentation; OWL; Semantic Web. IntroductionThe Web ontologies present several interests for the Web, such as annotating data, distinguishing between homonyms and polysemy, generalizing or specializing concepts, driving intelligent user interfaces and even inferring entirely new (implicit) information [3] [4]. Ontologies are created by ontology engineers with the help of domain experts [5]. Let us take as an example an ontology representing a population. This ontology should have information about the citizens, their dates of birth, relationships, hobbies, addresses, competences, jobs, etc. It seems to be a great ontology allowing us to get new information about one person's tendencies, how these tendencies may be affected by his relationships; also, the companies can use it to target their advertisements. But, for one reason or another, we may not be concerned by the population under a certain age, or may be interested only in a particular city's population. For such purposes, we assume that segments of such big ontologies that contain only the desired information will respond better to the users' expectations.Several studies are focused on the extraction, classification [6] [7] and segmentation [3] [8] of data in Web ontology; these data can be represented in the ontology web. For example, N. Gherabi et al [9] present a new approach to mapping data stored in relational databases in the semantic Web, it uses simple mappings based on certain specifications of the database schema and explain how relational databases can be used to define a mapping mechanism between the relational database and the OWL ontology. In another work [10], the authors have developed a method to convert UML schemas to Web Ontology.J. Seidenberg and A. Rector have presented in [3] a method for extracting small segments from large ontologies using the GALEN as an example. The segmentation algorithm they have presented was based on the classes hierarchy, which leads to segmenting the ontology by extracting a specific class hierarchy. Such method responds to segmenting ontologies like the GALEN ontology where one could be interested in a concept like HEART and all its super classes and subclasses.