Prediction of survival of heart failure patients: An application of classification-based machine learning algorithms

Kardiyo-vasküler hastalıklar dünya genelinde en çok ölüme sebep olan hastalıklar arasında yer almaktadır. Bir kardiyo-vasküler hastalık olan kalp yetmezliği, kalbin vücudun ihtiyaç duyduğu kanı pompalayamaması durumudur. Ülkemizde sıklıkla görülen bu hastalığın sonucu olarak ölümler yaşanmaktadır. Bu çalışmada kalp yetmezliğe sahip hastaların sağ kalım veya ölüm durumlarının tahmin edilmesi için makine öğrenmesi tabanlı bir yaklaşım önerilmektedir. Üç farklı sınıflandırma algoritması kullanılarak önerilen yöntemin etkinliği değerlendirilmektedir. Gerçekleştirilen deneylerde, Yapay Sinir Ağı algoritması ile en yüksek doğruluk değerine (86.67%) ulaşılmıştır. Önerilen yöntem, ölüm riskinin yüksek olduğu kalp yetmezliği hastalarına daha etkin ve uygun tedavi planlarının hazırlanması açısından yol gösterici olacaktır.Cardio-vascular diseases are among the diseases that cause the most deaths worldwide. Heart failure, a cardiovascular disease, is a condition in which the heart cannot pump the blood that the body needs. Deaths occur as a result of this disease, which is frequently seen in our country. In this study, a machine learning-based approach is proposed to predict survival or death of patients with heart failure. The effectiveness of the proposed method is evaluated using three different classification algorithms. In the experiments performed, the highest accuracy values (86.67%) was achieved with the Artificial Neural Network algorithm. The proposed method will guide the preparation of more effective and appropriate treatment plans for heart failure patients with a high risk of death

An F-Score-Weighted Indoor Positioning Algorithm Integrating WiFi and Magnetic Field Fingerprints

Indoor positioning systems have attracted much attention with the recent development of location-based services. Although global positioning system (GPS) is a widely accepted and accurate outdoor localization system, there is no such a solution for indoor areas. Therefore, various systems are proposed for the indoor positioning problem. Fingerprint-based positioning is one of the widely used methods in this area. WiFi-received signal strength (RSS) is a frequently used signal type for the fingerprint-based positioning system. Since WiFi signal distribution is nonstationary, accuracy is insufficient. Therefore, the performance of indoor positioning systems can be enhanced using multiple signal types. However, the positioning performance of each signal type varies depending on the characteristics of the environment. Considering the variability of the performances of different signal types, an F-score-weighted indoor positioning algorithm, which integrates WiFi-RSS and MF fingerprints, is proposed in this study. In the proposed approach, the positioning is first performed by maximum likelihood estimation for both WiFi-RSS and magnetic field signal values to calculate the F-score of each signal type. Then, each signal type is combined using F-score values as a weight to estimate a position. The experiments are performed using a publicly available dataset that contains real-world data. Experimental results reveal that the proposed algorithm is efficient in achieving accurate indoor positioning and consolidates the system performance compared to using a single type of signal

A MULTI-CRITERIA HEURISTIC ALGORITHM FOR PERSONALIZED

This paper proposes a heuristic function for multi-criteria route planning problems. The Analytical Hierarchy Process (AHP) is used for the multi-criteria aggregation process both for actual and heuristic cost functions. Travel distance, travel time, safety and fuel consumption are considered to be the selected criteria. Additionally, while considering real data sets, road safety and fuel consumption models are developed. The proposed multi-criteria heuristic function is consistent; therefore, the A* algorithm finds optimal routes. The proposed algorithm is tested and compared with existing algorithms in the literature using a real dataset for a specific region in Eskisehir, Turkey

Decorin, Tenascin C, Total Antioxidant, and Total Oxidant Level Changes in Patients with Pseudoexfoliation Syndrome

Purpose. Pseudoexfoliation syndrome (PEX) is an eye disease that develops under the influence of regional population differences, genetic factors, age, and environmental factors and is characterized by visualization of a gray-white fibrogranular substance in the lens anterior capsule and/or pupil margin during anterior segment examination. The underlying biochemical mechanisms of the disease have not yet been fully elucidated. Therefore, this study was designed to show the changes in aqueous humor and blood serum levels of matrix metalloproteinases (decorin and tenascin C), total antioxidants (TAS), and total oxidants (TOS) in both cataract patients who have unilateral PEX material and cataract patients who do not have unilateral PEX material. Methods. Biological samples were simultaneously collected from 22 cataract patients who had unilateral pseudoexfoliation (PEX patients) and 22 cataract patients who did not have unilateral pseudoexfoliation (control patients). From the collected biological samples, decorin (DEC) and tenascin C (TN-C) were measured with the enzyme-linked immunosorbent assay (ELISA) method, and TAS and TOS were measured with an autoanalyzer. Results. When decorin, tenascin C, and TOS values of PEX patients were compared with those of control patients, there was a statistically significant increase in all three parameters. Conversely, TAS values showed a statistically significant decrease in PEX patients compared to controls. DEC, TN-C, TAS values, and TOS values were significantly higher in aqueous fluid than in blood in both the PEX patient and control groups. Conclusions. We suggest that parameters such as DEC, TN-C, TAS, and TOS play a role in the etiopathology of pseudoexfoliation syndrome. Thus, bringing these increased levels of extracellular proteins and TOS and decreased levels of TAS back to within physiological limits can mediate the reorganization of the blood-aqueous fluid barrier and slow the progression of pseudoexfoliation syndrome

Clinical characteristics of and services provided for patients with lymphedema referred to a physiotherapy program during the years 2009 through 2019

Background: Lymphedema is a chronic and progressive disease whose diagnosis involves determination of clinical and demographic characteristics. The aim of this retrospective study was to analyze the clinical characteristics of patients with lymphedema and their various diagnoses. We studied patients who were referred for physiotherapy services at any point during the years 2009 through 2019. Methods: Retrospective data were collected from the files of 430 lymphedema patients. The type, cause, localization, stage, and severity of lymphedema and physiotherapy needs were analyzed and reported. Results: Primary and secondary lymphedema were observed in 18 (4.2%) and 412 (95.8%) patients, respectively. The patients' mean body mass index score was 30.66 kg/m(2). The data indicated that the most common cause of secondary lymphedema was breast cancer and its treatments (n = 196, 47.6%). Other causes were chronic venous insufficiency (CVI) (n = 140, 34%), lipolymphedema (n = 11, 2.7%), and other types of cancers (n = 65, 15.7%). According to the affected body regions, 416 patients had unilateral/bilateral upper and lower extremity lymphedema and 14 had head and neck lymphedema. The patients were followed with a home-based physiotherapy program (n = 353, 82.1%) or they underwent treatments through an outpatient program (n = 77, 17.9%). Conclusions: Most patients admitted to the clinic had a diagnosis of breast cancer and CVI. The severity and stages of lymphedema were variable. The data indicated that most patients were followed through a home-based physiotherapy program. These results may set a frame for understanding the treatment and care needs of patients with lymphedema