Chronic kidney disease (CKD) is still a health concern despite advances in surgical care and treatment. CKD’s growth in recent years has gained much interest from researchers around the world in developing high-performance methods for diagnosis, treatment and preventive therapy. Improved performance can be accomplished by learning the features that are in the concern of the problem. In addition to the clinical examination, analysis of the medical data for the patients can help the health care partners to predict the disease in early stage. Although there are many tries to build intelligent systems to predict the CKD by analysis the health data, the performance of these systems still need enhancement. This Paper proposes an intelligent classification and prediction model. It uses modified Deep Belief Network (DBN) as classification algorithm to predict the kidney related diseases and the Softmax as activation function and the Categorical Cross-entropy as a loss function. The evaluation of the proposed model shows that the model can predict the CKD with accuracy 98.5% and sensitivity 87.5 % comparing with existing models. Result analysis proves that using advanced deep learning techniques is beneficial for clinical decision making and can aid in early prediction of CKD and its related phases that reduces the progression of the kidney damage.

The World Health Organization (WHO) indicates that the proportion of the elderly will soon include nearly a quarter of the world population. Ensuring that health systems are prepared to deal with this phenomenal rate of aging and associated diseases generates many challenges. Among these challenges is facing Alzheimer’s Disease (AD) that may occur at some point in the elderly life and may harm societies. AD is considered a neurological, psychological, mental, and health setback. The Clinical Decision Support System (CDSS) can improve patient care and support many medical functions, such as diagnosing diseases that can reduce preventable harm. This research’s main objective is to design, implement, and evaluate the Alzheimer’s Disease Diagnosis Ontology (ADDO). It is a comprehensive semantic knowledge base toward the development of fuzzy ontology-based CDSS for AD diagnosis. ADDO can serve as a core component of CDSS, which provides representation, annotation, and access to aspects related to AD’s study and diagnosis. Toward the management of this objective, ADDO is based on the essentials of the Open Biomedical Ontology (OBO) and follows the Basic Formal Ontology (BFO) and the Ontology for General Medical Sciences (OGMS) principles. ADDO focuses on representing the patient characteristics, complications, drugs, diagnosis examination tests, and key aspects of their periodic visits in a standard way. The possibility of semantic interoperability is taken into account by integrating ADDO and a heterogeneous AD dataset. We used ADNI as a case study to mapping a set of real instances. To manage the medical domain’s uncertainty, ADDO is extended to fuzzy ontology to accommodate the medical linguistic variables and enhance diagnosis results’ efficiency. ADDO is constructed using Protégé 5.5.0 software and evaluated using the HermiT reasoner and SPARQL semantic queries. ADDO currently includes 7060 concepts, 99 properties, 46274 axioms, and 30708 annotations. As a result, ADDO is consistent and more reliable in managing most AD aspects than other existing AD ontologies.

In recent years, medical data have vastly increased due to the continuous generation of digital data. The different forms of medical data, such as reports, textual, numerical, monitoring, and laboratory data generate the so-called medical big data. This paper aims to find the best algorithm which predicts new medical data with high accuracy, since good prediction accuracy is essential in medical fields. To achieve the study’s goal, the best accuracy algorithm and least processing time algorithm are defined through an experiment and comparison of seven different algorithms, including Naïve bayes, linear model, regression, decision tree, random forest, gradient boosted tree, and J48. The conducted experiments have allowed the prediction of new medical big data that reach the algorithm with the best accuracy and processing time. Here, we find that the best accuracy classification algorithm is the random forest with accuracy values of 97.58%, 83.59%, and 90% for heart disease, M-health, and diabetes datasets, respectively. The Naïve bayes has the lowest processing time with values of 0.078, 7.683, and 22.374 s for heart disease, M-health, and diabetes datasets, respectively. In addition, the best result of the experiment is obtained by the combination of the CFS feature selection algorithm with the Random Forest classification algorithm. The results of applying RF with the combination of CFS on the heart disease dataset are as follows: Accuracy of 90%, precision of 83.3%, sensitivity of 100, and consuming time of 3 s. Moreover, the results of applying this combination on the M-health dataset are as follows: Accuracy of 83.59%, precision of 74.3%, sensitivity of 93.1, and consuming time of 13.481 s. Furthermore, the results on the diabetes dataset are as follows: Accuracy of 97.58%, precision of 86.39%, sensitivity of 97.14, and consuming time of 56.508 s.

Document classification has become an important field of research due to the increase of unstructured text documents available in digital form. It is considered one of the key techniques used for organizing the digital data by automatically assigning a set of documents into predefined categories based on their content. Document classification is a process that consists of a set of phases, each phase can be accomplished using various techniques. Selecting the proper technique that should be used in each phase affects the efficiency of the text classification performance. The aim of this paper is to present a classification model that supports both the generality and the efficiency. It supports the generality through following the logical sequence of the process of classifying the unstructured text documents step by step; and supports the efficiency through proposing a compatible combination of the embedded techniques for achieving better performance. The experimental results over 20-Newgroups dataset have been validated using statistical measures of precision, recall, and f-score. The results have proven the capability of the proposed model to significantly improve the performance.