Overview of artificial neural network in medical diagnosis – Pubrica

1. OVERVIEW OF ARTIFICIAL NEURAL NETWORK IN MEDICALDIAGNOSIS An Academic presentationby Dr.NancyAgens,Head,TechnicalOperations,Pubrica Group: www.pubrica.com Email:sales@pubrica.com

2. Today'sDiscussion Outline In-Brief Introduction Artificial NeuralNetwork Architecture OverviewofArtificialNeuralNetworkinMedicalDiagnosis CardiovascularDiseases Cancer Diabetes Conclusion

3. In-Brief A massive volume of clinical data is produced daily that possess minute andcritical information as well as varied, in-depth concepts of biochemistry and the results of imaging devices. Information provided byeach kind of data must be evaluated and assigned for diagnostic processes. To simplify the diagnostic process andevade errors in that process, artificial intelligence techniques can be adoptedlikecomputer- aided diagnosis and artificial neural networks. The biostatistical servicesmachine learning algorithms can deal with a broad set of specific dataandproduce categorized outputs by checking the blogs inPubric.

4. The artificial neural network has been widely usedin the fields of science and technology. It is used for the optimization ofdata. It predicts the outputs using the input data in fieldslike chemical engineering, biotechnology, healthcare, agriculture, etc., which all handles varied sets ofdata. The artificial neural network can be used formodelling non-linear systems with a complex system ofvariables. Thus, most of the chemical engineering andbiological processes are modelled using Artificial neural network with the help of biostatistical consultingservices. Introduction

5. Clinical biostatistics servicesstate that Artificial neural network is the simulation of human neuralarchitecture. Artificial Neural Network The learning and generalization potentials ofhuman neural network inspired for the development of an artificial neuralnetwork. It works by taking the 70% of input data to build a network then takes the remaining 15% data to trainitself and at last utilize the remaining 15% data to test itself and eventually produce the optimizedoutputs.

6. The artificial neural networkis made up of three layers, viz., – (i) input layer, (ii) hidden layer,(iii) outputlayer. The schema of the neurons built inside thenetwork is based upon the complexity of thesystem. The input layer collects the input data andtransfers to the hidden layer where the data is processed to produce optimized results with statistical programming services. Architecture

7. Every Artificial neural network has an activation function that is used for determining theoutput. Each neuron is interconnected, and each connection has a weight attached possessing either positive or negative value which tends to change upon the training thenetwork.

8. Overview ofArtificial Neural Networkin Medical Diagnosis Seeking various uses in various fields ofscience, medical diagnosis field also has found theapplication of artificial neural network using biostatistics in clinicalservices. It is used in the diagnosis of cancer,sclerosis, diabetes, heart diseases,etc. An adaptive algorithm is developed and applied to yield maximum accuracy in outputs with thestatistics i n clinicaltrials.

9. It is the collection of diseases affecting theheart, cardiac muscles, blood vessels,veins. Cardiovascular Diseases National centre of health statistics reported that leading cause of death in united statesof America is these cardiovasculardiseases. In the past, the data collected from thepatients were used to develop an Artificial neural network model with the backpropagation algorithm wasdeveloped. Contd..

10. This model was able to achieve 91.2% accuracy in the diagnosis of thesediseases from the datacollected. There were other models with less than 90% accuracy also usedto diagnosespecific types of heartdiseases. Contd..

12. In 2012, reports of American cancer societysaid that more than 1.6 million newly diagnosed cases werefound. Hence, there was the need to develop a rapidand appropriate diagnosis for clinicalmanagement. The pertinent information for diagnosis was collected from the advanced analytical methodslike mass spectrometry and applied in the clinical diagnosis of breast and ovariancancer. Contd.. Cancer

13. Artificial neural network is also used to develop in diagnosing the different typesof brain tumours, lungcarcinoma. Ultimately, Artificial neural network was seen using the ground-level datathat ranges from clinical data to results of biochemical assays and providing maximum diagnostic accuracy for different types ofcancer.

14. Diabetes Diabetes has become a severe health riskissue in both developed and developing countries that reaching an estimate of 366 million diabetes casesglobally. Type ii diabetes is the standard type of this disease which is due to the impropercellular response to insulin which leads to hyperglycemia. Contd..

15. The information of parameters like age, gender, weight and glucose level were collected and used as input data for building an Artificial neural networkwhichcould able to produce results with 90%accuracy. Artificial neural networks are used to track the level of glucose as wellas diagnosing diabetes according to biostatistical research for clinicaltrials.

16. The artificial neural network can be inferred as apowerful tool in clinical management of diseases with several advantages like the capability of processing a vast set of data, reducing the processing time, ability to produce optimized results with maximumaccuracy. Nevertheless, Artificial neural network can be used onlyas tool aiding in diagnosis done by the clinical physician, says biostatistical CRO, who is responsible for critical evaluation of theresults. Pubrica helped to understand the role of ANN tool inthe medicalfield. Conclusion

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