Back Propagation For Classification ( Research Essay )

The objective of the neural network is to transform the input to meaningful output. Neural networks are often used for statistical analysis and data modeling. Neural network has many uses in data processing, robotics, and medical diagnosis [2]. From the starting of the neural network there are various types found, but each and every types has some advantages and disadvantages. Deep learning and -neural network software are the categories of artificial neural network. The parallel process also allows ANNs to process the large amount of data very efficiently. The artificial neural network is built with a systematic

The processing unit of a single - layer perceptor network can be able to solve the

Based on Chapter 2, Neural Network Method (NN) will be chosen for voice-based command recognition method because it can handle bigger databased. For Neural Network to implement pattern recognition is quite common, and beneficial to use is backpropagation. Supervised learning that starts by inputting the training data through the network is a form of this method. When the data is put in the network, it will generate propagation output activations and then propagated backwards through the neural network, and generating a delta value for all hidden and output neuron. The weights of the network are then update by calculated delta values that generate by neural network, which increase the speech and quality of the learning process.

Mammography is a popular technique but it has its limitations especially in younger women and in denser breasts. The Computer-Aided-Diagnosis has been proposed for the medical prognosis [7-9]. The fuzzy logic and Artificial Neural Network form the basis of the intelligent systems. There are several instances where the artificial intelligence is used for the diagnosis of the breast cancer. The methods have included many Artificial Neural Networks architectures such as Convolution Neural Network [10], Radial Basis Network [11], General Regression Neural Network [11], Probabilistic Neural Network [11], Resilient Back propagation Neural Network [12], and hybrid with Fuzzy Logic [13]. In this paper [7] a supervised artificial neural network [14-16] was used to help classify the breast lesions into malignant and benign classes by processing the computer cytology images. The accuracy of the trained neural network was found to be 82.21%. The ANN has been established as a robust system for the diagnosis of breast cancer [18].There is a complex relationship between different biomarkers which were identified for the diagnosis of this cancer [19], the MLP neural network was simulated for the diagnosis using four biomarkers

The article, “How Predictive Analytics Are Transforming Health Care,” has a big vision for health care researchers and practitioners as well as reducing the hospital readmissions. These predictive analytics have a positive impact for the health care systems and have a potential to transform the health care system towards the information technology. These also help to advance the practitioners’ awareness on decisions making towards data sources. As the article mentioned, the predictive analytics will help to predict critical illnesses and try to find the solutions as quickly as possible. At the same time, large hospitals and most health care organizations have already implemented the electronic health records and accessed their patient’s data

Data mining for healthcare is useful in evaluating the effectiveness of medical treatments and it is interdisciplinary field of study that has its roots in databases statistics machine learning and data visualization. Diabetic disease refers to the heart disease that develops in persons with diabetes. The term diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin. The cardiovascular disease is class of diseases that involves the heart or blood vessels Even though many data mining classification techniques exist for the prediction of heart disease there is insufficient data for the prediction of heart diseases in a diabetic individual. The main objective focus on this research is to find an optimal

Knowledge attained wth the use of data mining techniques can be used to make innovative and successful decisions that will increase the success rate of health care sector and the health of patients. In this paper, the study of classification algorithms in data mining techniques and its applications are discussed. The popular classification algorithms used in healthcare domain are explained in detail. The open source data mining tools are discussed. The applications of healthcare sector using data mining techniques are studied. With the future development of information communication technologies, data mining will attain its full potential in the discovery of knowledge hidden in the health care organizations and medical

Abstract - The healthcare industry collects large amounts of Healthcare data, but unfortunately not all the data are mined which is required for discovering hidden patterns and effective decision making. We propose efficient genetic algorithm with the back propagation technique approach for heart disease predic-tion. This paper has analyzed prediction systems for Heart dis-ease using more number of input attributes. The System uses medical terms such as Gender, blood pressure, cholesterol like13 attributes to predict the likelihood of patient getting a Heart dis-ease.

Predictive modeling is another form of data analytics that is focused on forecasting the future medical costs. This model utilizes patients’ medical information in an effort to evaluate health risks and forecast the future of medical utilization (Ingenix, 2006). A large variety of predictive modeling procedures are available, which are created to assign a specific risk level or score to the patients (Asparouhov, 2012). Risk scores are controlled by the risk markers and are given to each patient in a specific population (Ingenix, 2006). In using any past diagnoses and other information gathered, the predictive models will calculate the individual patient costs that can then be used by healthcare providers and the insurers. Therefore, certain

A neural network’s accuracy depends on the data provided to the network. In this case, in order

But due to partially complete medical data sets, accuracy often decreases, results in more number of misclassification that can lead to harmful complications. An accurate prediction and diagnostic of a disease becomes a challenging research problem for many researchers. Therefore, aimed to improve the diagnosis accuracy we have proposed a new methodology, based on novel preprocessing techniques, and K-nearest neighbor classifier. The effectiveness of proposed methodology is validated with the help of various quantitative metrics and a comparative analysis, with previous reported studies using the same UCI dataset focusing on pimadiabetes disease diagnosis.

Cross-validation method was used to evaluate the predictive power of models. Dataset was equally divided into two parts. Models were fitted on training set and tested on testing set.

Medical diagnostic system is a branch in bioinformatics that is concerned with classifying medical records. Breast cancer is the most common deployed cancer in females worldwide. The main obstacle is the vagueness and ambiguity involving the breast cancer data. Human nature handles the vagueness and ambiguity easily. Therefore, doctors diagnose the patient condition using their expertise. Fuzziness and rough boundary theories simulate the human thinking. The fuzzy rough hybrids address the uncertainty in terms of membership degree of truth and lower and upper boundaries of fuzzy rough set theory. This research solves the diagnostic breast cancer problems via a proposed hybrid model of fuzzy rough feature selection and rough neural networks. The medical data is preprocessed by the fuzzy rough feature selection algorithm to remove unnecessary attributes. The reduced data set is applied to the rough neural network to learn the connection weights iteratively. The test data set are used to measure the proposed model accuracy and time complexities. Lower and upper approximations of the input features are weighted by input synapses learnt through training phase. The fuzzy rough proposed model design and implementation are declared. The experiments used WDBC and WPBC data sets from the UCI machine learning repository. The experimental results proved the fuzzy rough model ability to classify new instances compared with the conventional neural network.

comparison of mean square error using feed forward back propagation (FFBP) and radial basis function(RBF) neural network algorithms are given in table 5.2 for analysis of band pass FIR filter with hanning window.

A. Forward Propagation and Backpropagation In case of forward propagation each node has the same classifier and none of them are fired randomly. Also repeating the input provides the same output. The question that arises at this point is if every node in the hidden layer receives same input, why dont all of them produce the same output?The reason is each set of input is modified by unique weights and biases [6]. Each edge has a specific weight and each node has a unique bias.