Classifying anemia types using artificial learning methods

The most common blood disease worldwide is anemia, defined by the World Health Organization as a condition in which the red blood cell count or oxygen-carrying capacity is insufficient. As both a disease and a symptom, this condition affects the quality of life. Early and correct diagnosis of the type of anemia is vital in terms of patient treatment. The increasing number of patients and hospital priorities, as well as difficulties in reaching medical specialists, may impede such a diagnosis. The present work proposes a system that will enable the recognition of anemia under general clinical practice conditions. For this system, a model constructed using four different artificial learning methods. Artificial Neural Networks, Support Vector Machines, Naive Bayes, and Ensemble Decision Tree methods are used as classification algorithms. The models are evaluated with a dataset of 1663 samples and used 25 attributes, including hemogram data and general information such as age, sex, chronic diseases, and symptoms to diagnose 12 different anemia types. Data are collected by examining patient files at a university hospital in Turkey. In addition to all the data used by the doctors, the model also utilized eight different datasets created via particular feature selection techniques. The interface is designed to provide decision support to both medical consultants and medical students. Data are classified using the four different algorithms and an acceptable success ratio is obtained for each. Each model is validated using Classification Error, Area Under Curve, Precision, Recall, and F-score metrics in addition to Accuracy values. The highest accuracy (85.6%) achieved using Bagged Decision Trees, followed by Boosted Trees (83.0%) and Artificial Neural Networks (79.6%). (C) 2020 Karabuk University. Publishing services by Elsevier B.V.Research Fund of Sakarya University, Turkey [2015-50-02-010]This work was supported by the Research Fund of Sakarya University, Turkey, under Project Number: 2015-50-02-010. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.WOS:0006152325000062-s2.0-8509898523

Diagnostic Accuracy Comparison of Artificial Immune Algorithms for Primary Headaches

The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: migraine, tension, cluster, and other primary headaches. After we took this main objective into consideration, three different neurologists were required to fill in the medical records of 850 patients into ourweb-based expert system hosted on our projectweb site. In the evaluation process, Artificial Immune Systems (AIS) were used as the classification algorithms. The AIS are classification algorithms that are inspired by the biological immune system mechanism that involves significant and distinct capabilities. These algorithms simulate the specialties of the immune system such as discrimination, learning, and the memorizing process in order to be used for classification, optimization, or pattern recognition. According to the results, the accuracy level of the classifier used in this study reached a success continuum ranging from 95% to 99%, except for the inconvenient one that yielded 71% accuracy