Blood analysis is an essential indicator for many diseases; it contains several parameters which are a sign for specific blood diseases. For predicting the disease according to the blood analysis, patterns that lead to identifying the disease precisely should be recognized. Machine learning is the field responsible for building models for predicting the output based on previous data. The accuracy of machine learning algorithms is based on the quality of collected data for the learning process; this research presents a novel benchmark data set that contains 668 records. The data set is collected and verified by expert physicians from highly trusted sources. Several classical machine learning algorithms are tested and achieved promising results.

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This research argues that providing the students with the same material in different methods that correspond to their skills would guarantee their satisfaction as well as their level of success. This research focuses on the vital exploration of the suitable student learning style with respect to the student skills, the type of material and the impact of intelligent techniques. The research scope considers that students' skills are normally varied among individuals, this variation should be considered in the learning process. The proposed approach is based on the successful migration of different data across the components and a formal description of this data was presented to clarify the homogenous transformation according to the applied steps. The proposed framework has been applied on a set of students and the results revealed to a raise in the students' performance represented in their grades and their satisfaction level.

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Evaluating Algorithms is one of the critical steps which should be strongly considered as this is the pillar of most of the decisions. This research proposes a novel method for accurate algorithms’ evaluation according to the metrics’ relationships and weight. The weight of the evaluation metrics is determined according to their invariance level. The proposed method validity is confirmed by applying and evaluating the most famous and well-populated classification techniques. The results have been considered according to the calculated weight of the evaluation measures to reveal the final algorithm evaluation. As a case study, the most suitable classification technique for Tinnitus data are explored. This research considered the Tinnitus data as Tinnitus symptoms are not clearly recognized which highlights the difficulty of the patients to have a direct and fast diagnosis which highlighted the motivation in investigating intelligent methods for fast Tinnitus diagnosing. The research applied the experiment on a real dataset that is gathered in Egypt and the results highlighted that the Support Vector Machine classification algorithm is the most suitable technique for Tinnitus data classification with an accuracy equal to 90.1%.