Optimization of Backpropagation for Early Detection of Diabetes Mellitu

Diabetes mellitus is one of the urgent health problems in the world. Diabetes is a condition primarily defined by the level of hyperglycemia giving rise to risk of micro vascular damage. Those who suffer from this disease generally do not realize and tend to overlook the early symptoms. Late recognition of these early symptoms may drive the disease to a more concerning level. One solution to solve this problem is to create an application that may perform early detection of diabetes mellitus so that it does not grow larger. In this article, a new method in performing early detection of diabetes mellitus is suggested. This method is backpropagation with three optimization namely early initialization with Nguyen-Widrow algorithm, learning rate adaptive determination, and determination of weight change by applying momentum coefficient. The observation is conducted by collecting 150 data consisting of 79 diabetes mellitus patient and 71 non diabetes mellitus patient data. The result of this study is the suggested algorithm succeeds in detecting diabetes mellitus with accuracy rate of 99.33%. Optimized backpropagation algorithm may allow the training process goes 12.4 times faster than standard backpropagation

Comparative study on the performance of Au/F-TiO2 photocatalyst synthesized from Zamzam water and distilled water under blue light irradiation

Recurring problems of titanium dioxide (TiO2) for needing UV light to be activated and high electron-hole recombination rate limit the application of TiO2 as a prolific photocatalyst. By modifying the morphology and introducing electron trapping species into TiO2, the photocatalytic activity of TiO2 could be improved. Solvents of two different kinds; distilled water and Zamzam water were used in peroxotitanic acid synthesis of TiO2 and the photocatalyst was utilized to degrade Reactive Blue 19 (RB19) dye under blue light irradiation (475 nm) to assess the visible light activity of synthesized TiO2. Fluorine was incorporated to control the morphology while gold nanoparticles (GNP) stabilized by arabic gum were deposited to trap electrons. The morphology of F-TiO2 which appeared to be in ovoid shape was confirmed by Field Emission-Scanning Electron Microscope (FE-SEM) and Transmission Electron Microscope (TEM). Brunauer-Emmett-Teller (BET) surface area and crystallite size estimated from X-ray Diffraction (XRD) data revealed that F-TiO2 modified using HF was smaller in size and exhibited single anatase phase. The band gap of Au-TiO2 synthesized by distilled and Zamzam water was 2.78 eV and 2.89 eV respectively; shifted from 3.08 eV in blank TiO2. Peroxo Au/F-TiO2 synthesized with the incorporation of arabic gum as GNP stabilizer and HF as fluorine modifier degraded up to 49.23% of RB19 within two hours of reaction. The addition of fluorine and gold demonstrated high ability to enhance visible light activity of TiO2 with distilled water used as solvent displayed higher photocatalytic performance compared to Zamzam water

مقایسه مدل شبکه عصبی مصنوعی و درخت تصمیم برای شناسایی و پیش بینی عوامل مرتبط با دیابت نوع2

هدف:  یکی از اهداف تحقیقات پزشکی تعیین عوامل مرتبط در پیش ­بینی بیماری می ­باشد. یکی از شایع ­ترین بیماری های متابولیک در ایران، دیابت می­باشد. هدف از این مطالعه شناسایی عوامل موثر در پیش بینی دیابت با استفاده از مدل­ های شبکه عصبی مصنوعی و درخت تصمیم می ­باشد. روش بررسی:  برای انجام مطالعه، پرونده 901 تن از افرادی که در سال­ های 91 و 92 به مراکز بهداشتی شهر مشهد مراجعه کرده بودند، استفاده گردید. در ابتدا با استفاده از روش­ های آمار توصیفی و تحلیلی، داده ­ها آنالیز شدند. سپس 70% داده­ ها به طور تصادفی برای ساخت مدل­ های شبکه عصبی مصنوعی و درخت تصمیم انتخاب شدند. 30% باقیمانده برای مقایسه عملکرد مدل ­ها استفاده شد. در نهایت عملکرد مدل ­ها با استفاده از سطح زیر منحنی راک (ROC) مورد مقایسه قرار گرفت. یافته ­ها:  توسعه دو مدل پیش ­بینی با استفاده از 14 متغیر انجام شد. دو مدل از نظر سطح زیر منحنی راک، حساسیت، ویژگی و صحت مورد ارزیابی قرار گرفتند. برای مدل شبکه عصبی، سطح زیر منحنی راک و حساسیت به ترتیب 69/1 و 74/2 بدست آمد. برای مدل درخت تصمیم نیز سطح زیر منحنی راک و حساسیت به ترتیب 68/9 و 64/77  بدست آمد. در هر دو مدل متغیرهای سابقه خانوادگی دیابت، تری گلیسرید، شاخص توده بدنی، لیپوپروتئین با چگالی کم و فشار خون سیستولیک و دیاستولیک مهم ترین عوامل مرتبط در شناسایی دیابت نوع 2 بودند.      نتیجه‌ گیری:  نتایج نشان داد که مدل شبکه عصبی چند لایه سطح زیر منحنی راک بهتری نسبت به درخت تصمیم CART در پیش ­بینی دیابت نوع 2 دارد. همچنین لیپوپروتئین با چگالی کم مهم ­ترین عوامل مرتبط در شناسایی دیابت نوع 2 می ­باشد. مطالعه حاکی از آنست که روش ­های داده­کاوی نوین از جمله شبکه عصبی مصنوعی و درخت تصمیم می ­توانند برای شناسایی عوامل مرتبط با بیماری­ ها مورد استفاده قرار گیرند

Classification of Diabetes and Cardiac Arrhythmia using Deep Learning

Master's thesis Information- and communication technology IKT591 - University of Agder 2018Deep Learning (DL) is a research area that has ourished signi cantly in the recent years and has shown remarkable potential for arti cial intelligence in the eld of medical applications. The reasons for success are the ability of DL algorithms to model high-level abstractions in the data by using automatic feature extraction property as well as signi cant amount of medical data that is available for training these algorithms. DL algorithms can learn features from a large volume of healthcare data, and then use the procured insights to assist clinical practice. We have implement DL algorithm for the classi cation of two diseases in the medical domain: Diabetes and Cardiac Arrhythmia. Diabetes is often considered as one of the world's major health problems according to the World Health Organization. Recent surveys indicate that there is an increase in the number of diabetic patients resulting in the increase in serious complications such as heart attacks and deaths. This thesis presents a Multi-Layer Feed Forward Neural Networks (MLFNN) for the classi cation of diabetes on publicly available Pima Indian Diabetes (PID) dataset. A series of experiments are conducted on this dataset with variation in learning algorithms, activation units, techniques to handle missing data and their impact on classi cation accuracy have been discussed. Finally, the results are compared with other machine learning algorithms like Na ve Bayes, Random Forest, and Logistic Regression. The achieved classi cation accuracy by MLFNN (82.5%) is the best of all the other classi ers. The term arrhythmia refers to any variation in the usual sequence of the heartbeat. There are many types of cardiac arrhythmia ranging in severity, including Premature Atrial Contractions (PACs), Atrial Fibrillation, and Premature Ventricular Contractions (PVCs). This thesis focuses on the use of DL algorithms: Convolutional Neural Network (CNN) and Long Short- Term Memory (LSTM) to classify arrhythmia with minimum possible data pre-processing on MIT-BIH Arrhythmia Database (MIT dataset). Furthermore, we study the in uence of di erent hyperparameters like L2 regularization and number of epochs on the classi cation accuracy of LSTM. We achieved a classi cation accuracy of 99.19% and 98.40% with CNN and LSTM models respectively. From our research, we believe that CNN model can assist the doctors in the classi cation of arrhythmia