No abstract

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.

Coronavirus (covid 19) is one of the most dangerous viruses that have spread all over the world. With the increasing number of cases infected with the coronavirus, it has become necessary to address this epidemic by all available means. Detection of the covid-19 is currently one of the world's most difficult challenges. Data science and machine learning (ML), for example, can aid in the battle against this pandemic. Furthermore, various research published in this direction proves that ML techniques can identify illness and viral infections more precisely, allowing patients' diseases to be detected at an earlier stage. In this paper, we will present how ontologies can aid in predicting the presence of covid-19 based on symptoms. The integration of ontology and ML is achieved by implementing rules of the decision tree algorithm into ontology reasoner. In addition, we compared the outcomes with various ML classifications used to make predictions. The findings are assessed using performance measures generated from the confusion matrix, such as F-measure, accuracy, precision, and recall. The ontology surpassed all ML algorithms with high accuracy value of 97.4%, according to the results.

Diabetes is one of the chronic diseases, which is increasing from year to year. The problems begin when diabetes is not detected at an early phase and diagnosed properly at the appropriate time. Different machine learning techniques, as well as ontology-based ML techniques, have recently played an important role in medical science by developing an automated system that can detect diabetes patients. This paper provides a comparative study and review of the most popular machine learning techniques and ontology-based Machine Learning classification. Various types of classification algorithms were considered namely: SVM, KNN, ANN, Naive Bayes, Logistic regression, and Decision Tree. The results are evaluated based on performance metrics like Recall, Accuracy, Precision, and F-Measure that are derived from the confusion matrix. The experimental results showed that the best accuracy goes for ontology classifiers and SVM.