Medical analysis and diagnosis by neural networks

In its first part, this contribution reviews shortly the application of neural network methods to medical problems and characterizes its advantages and problems in the context of the medical background. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic systems. Then, paradigm of neural networks is shortly introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described. Additionally, the problem of interfacing the network and its result is given and the neuro-fuzzy approach is presented. Finally, as case study of neural rule based diagnosis septic shock diagnosis is described, on one hand by a growing neural network and on the other hand by a rule based system. Keywords: Statistical Classification, Adaptive Prediction, Neural Networks, Neurofuzzy, Medical System

An Empirical Model for Thyroid Disease Classification using Evolutionary Multivariate Bayseian Prediction Method

Thyroid diseases are widespread worldwide. In India too, there is a significant problems caused due to thyroid diseases. Various research studies estimates that about 42 million people in India suffer from thyroid diseases [4]. There are a number of possible thyroid diseases and disorders, including thyroiditis and thyroid cancer. This paper focuses on the classification of two of the most common thyroid disorders are hyperthyroidism and hypothyroidism among the public. The National Institutes of Health (NIH) states that about 1% of Americans suffer from Hyperthyroidism and about 5% suffer from Hypothyroidism. From the global perspective also the classification of thyroid plays a significant role. The conditions for the diagnosis of the disease are closely linked, they have several important differences that affect diagnosis and treatment. The data for this research work is collected from the UCI repository which undergoes preprocessing. The preprocessed data is multivariate in nature. Curse of Dimensionality is followed so that the available 21 attributes is optimized to 10 attributes using Hybrid Differential Evolution Kernel Based Navie Based algorithm. The subset of data is now supplied to Kernel Based NaEF;ve Bayes classifier algorithm in order to check for the fitness

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

Hepatitis is a dreaded disease that has taken the lives of so many people over the recent past years. The research survey shows that hepatitis viral disease has five major variants referred to as Hepatitis A, B, C, D, and E. Scholars over the years have tried to find an alternative diagnostic means for hepatitis disease using artificial intelligence (AI) techniques in order to save lives. This study extensively reviewed 37 papers on AI based techniques for diagnosing core hepatitis viral disease. Results showed that Hepatitis B (30%) and C (3%) were the only types of hepatitis the AI-based techniques were used to diagnose and properly classified out of the five major types, while (67%) of the paper reviewed diagnosed hepatitis disease based on the different AI based approach but were not classified into any of the five major types. Results from the study also revealed that 18 out of the 37 papers reviewed used hybrid approach, while the remaining 19 used single AI based approach. This shows no significance in terms of technique usage in modeling intelligence into application. This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients

Fuzzy Expert System for Tropical Infectious Disease by Certainty Factor

Communication between doctor and patient play an important role in determining the diagnosis of the illness suffered by the patient. Consultation time constraints led to insufficient information obtained to produce a diagnosis. This limitation is overcome by developing an expert system using fuzzy logic to represent the vagueness of symptoms experienced by patients and the certainty factor represents a relationship between the symptoms and disease. Fuzzy logic method begins with the acquisition of knowledge to produce the facts and rules, implication process, composition and defuzzification. The result of defuzzification used in the calculation of sequential and combined certainty factor which represent the belief percentage of diseases diagnosis that suffered by the patient. The results of the expert diagnosis with expert system for the given cases indicates the system, has the similarity diagnosis with the expert at 93.99%

Implementation and Evaluation of A Type-1 Fuzzy Logic Controller for Healthcare Diagnosis and Monitoring

Type-1 fuzzy inference systems have shown potential to improve clinician performance by imitating human thought processes in complex circumstances and accurately executing repetitive tasks to which humans are ill-suited. This paper addresses the implementation of a type-1fuzzy model for pregnancy health risk diagnosis and monitoring to enhance control strategies in the medical discipline of diagnosis and monitoring pregnancy health conditions. Twenty-five pregnant patients are selected and studied and the observed results computed in the range of predefined limit by the domain experts. Both the design model and simulation result are same. The system is developed using NETBEANS IDE, JAVA, MYSQL, etc using Windows Vista as operating system platform. Results indicate that, the study has ascertained the association of the risk factors with pregnancy outcomes. It is observed that, the paper will serve as a tool for medical practitioners in educating the women more about the degree of influence of risk on pregnancy impacted by pregnancy risk factors. Thus encourage them to begin antenatal clinic early in pregnancy. It is believed that our application will reduce doctors’ workload during consultation and help to eradicate major negative pregnancy outcomes; thus promoting positive pregnancy outcomes. Keywords: Type-1 fuzzy inference system, Fuzzy logic decision support, Pregnancy health risk, Infant mortalit

Passively mode-locked laser using an entirely centred erbium-doped fiber

This paper describes the setup and experimental results for an entirely centred erbium-doped fiber laser with passively mode-locked output. The gain medium of the ring laser cavity configuration comprises a 3 m length of two-core optical fiber, wherein an undoped outer core region of 9.38 μm diameter surrounds a 4.00 μm diameter central core region doped with erbium ions at 400 ppm concentration. The generated stable soliton mode-locking output has a central wavelength of 1533 nm and pulses that yield an average output power of 0.33 mW with a pulse energy of 31.8 pJ. The pulse duration is 0.7 ps and the measured output repetition rate of 10.37 MHz corresponds to a 96.4 ns pulse spacing in the pulse train

Prediction of Severity of Diabetes Mellitus using Fuzzy Cognitive Maps

The objective to develop this research paper is concerned with a system which helps diagnose the severity of diabetes. The disease named diabetes mellitus makes the body unable to handle sugar so it causes thirst, frequency of urination, tiredness and many other symptoms. The diabetes mellitus describes a metabolic disorder characterized by chronic hyperglycemia with disturbances of carbohydrate, fat and protein metabolism resulting from defects in insulin secretion, insulin action, or both. It can be caused by number of factors like pancreatic dysfunction, obesity, hereditary, stress, drugs, alcohol etc. It includes long term damage, dysfunction and failure of various organs. The effects of diabetes mellitus include long term damage and failure of various organs. Diabetes mellitus may present with characteristic symptoms such as thirst, polyuria, blurring of vision, and weight loss. This Paper is implemented on soft computing technique, namely Fuzzy Cognitive Maps (FCM) to find out the presence or absence of diabetes mellitus based on the input of sign/symptoms recorded at three fuzzy levels developed by the domain experts. The large amount of data and information that needs to be handled and integrated requires specific methodologies and tools. The FCM based decision support system was developed with a view to help medical and nursing personnel to assess patient status assist in making a diagnosis. The software tool was tested on 50 cases, showing results with an accuracy of 96%. The analysis of experimental results of different applicants checks the correctness and consistency of decision Support system for correct decision making. Keywords: Fuzzy Logic, FCM, Diabetes Mellitus, Prediction, Symptoms

Predicting the outcomes of traumatic brain injury using accurate and dynamic predictive model

Predictive models have been used widely to predict the diseases outcomes in health sector. These predictive models are emerged with new information and communication technologies. Traumatic brain injury has recognizes as a serious and crucial health problem all over the world. In order to predict brain injuries outcomes, the predictive models are still suffered with predictive performance. In this paper, we propose a new predictive model and traumatic brain injury predictive model to improve the predictive performance to classifying the disease predictions into different categories. These proposed predictive models support to develop the traumatic brain injury predictive model. A primary dataset is constructed which is based on approved set of features by the neurologist. The results of proposed model is indicated that model has achieved the best average ranking in terms of accuracy, sensitivity and specificity