# Application of Neural Networks in Diagnosing Cancer Disease Using Demographic Data

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3 Background: Neural Network The construction of the neural network involves three different layers with feed forward architecture. This is the most popular network architecture in use today. The input layer of this network is a set of input units, which accept the elements of input feature vectors. The input units(neurons) are fully connected to the hidden layer with the hidden units. The hidden units (neurons) are also fully connected to the output layer. The output layer supplies the response of neural network to the activation pattern applied to the input layer. The information given to a neural net is propagated layer-by-layer from input layer to output layer through (none) one or more hidden layers. Important issues in MultiLayer Perceptrons (MLP) design include specifications of the number of hidden layers and the number of units in these layers. The number of input and output units is defined by the problem the number of hidden units of use is far from clear. That is the amount of hidden layers and their neurons is more difficult to determine. A network with one hidden layer is sufficient to solve most tasks. The universal approximation theorem for neural networks states that every continuous function that maps intervals of real numbers to some output interval of real numbers can be approximated arbitrarily closely by a multi-layer perception with just one hidden layer. There is no theoretical reason ever to use more than two hidden layers. It is also been seen that for the vast majority of principal problems.those problems that require two hidden layers are only rarely encountered in real life situations. Using more than one hidden layer is almost never beneficial. It often slows dramatically when more hidden layers are used.none of the known problems needs a network with more than three hidden layers in order to be solved error. Choosing an appropriate number of hidden nodes is important. A common approach is to start with a larger number of nodes and then employ network pruning algorithms for optimization. An excessive number of hidden neurons may cause a problem called overfitting. L Rawtani (2001) suggested choosing the number of hidden nodes should be one or more than the training points on the curve. The number of neurons in the hidden layers may equivalent to the control points. Here the average number of control points (4) has been assigned as the number of hidden neurons. The Model Artificial Neural Network model could perform "intelligent" tasks similar to those performed by the human brain. Artificial neural network models offer a completely different approach to problem solving and they are sometimes called the sixth generation of computing. In the network the input neuron values are the demographic data concerns information such as patient s age, sex etc. The hidden neuron values are based on heuristic diagnostic knowledge represents experience accumulated through years and concerns the way an expert uses the patient data to make diagnoses. The heuristic knowledge has been acquired interviewing experts in the field and constructed a diagnostic tree based on criteria. A feature vector x consists of n components denoted by x 1, x 2, x 3. where x i represents a variable that is relevant to the classification problem. Nominal variables are used to represent the input values in the nodes of the input layer. Nominal variables may be two-state or many-state. A twostate nominal variable is easily represented by transformation into a numeric value. In this number of numeric variables represent the single nominal variable. The number of numeric variables equals the number of possible values; one of the N variables is set and the others are cleared. Neural networks has facilities to convert both two-state and many-state nominal variables.they can be represented using an encoding known as one-of-n encoding. The hidden layer contains four hidden neurons h 1, h 2, h 3 and h 4 which represent the symptoms of lung cancer. The output layer contains two neurons that produce the binary outputs. Training the model Once a network has been structured for a particular application, that network is ready to be trained. To start this process the initial weights are chosen randomly. Then, the training, or learning, begins. The ANN has been trained by exposing it to sets of existing data (based on the follow up history of cancer patients) where the outcome is known. Multi-layer networks use a variety of learning techniques; the most popular is back propagation algorithm. It is one of the most effective approaches to machine learning algorithm developed by David Rummelhart and Robert McLelland (1994). Information flows from the direction of the input layer towards the output layer. A network is trained rather than programmed. Learning in ANN s is typically accomplished using examples. This is also called training in ANN s because the learning is achieved by adjusting the connection weights in ANN s iteratively so that trained (or learned).the number of iterations of the training algorithm and the convergence time will vary depending on the weight initialization. After repeating this process for a sufficiently large number of training cycles the network will usually converge to some state where the error of the calculations is small. In this case one says that the network has learned a certain target function. Learning techniques are often divided into supervised, unsupervised and reinforcement learning. 78

4 The neural network structure is shown in the following figure: Input Layer a g e - - Hidden Layer Output layer g g sm o ke al hol cho Addl. node 79

6 commands.. It includes high-level functions for twodimensional and three-dimensional data visualization, image processing, animation, and presentation graphics. Initially 100 lung cancer patients data has been collected from various hospitals and trained with the neural networks International Journal of Computer Applications ( ) PATIENT"s DETAILS (Age,Gen,Smok) It gives more than 87% of accuracy.the present algorithm is fast,taking only few seconds of execution time.the results are found to be better using back propagation algorithm. In this paper, an attempt has been made to make use of neural networks in the medical field 1 zaxis-->smoking Yaxis-->GENDER Xaxis-->AGE 8 10 PATIENT"s DETAILS(Age,Gen,Alco) 1 zaxis-->alcohol Yaxis-->GENDER Xaxis-->AGE 8 10 CONCLUSION Lung cancer is one of the most common and deadly diseases in the world.detection of lung cancer in its early stage is the key of its cure. The automatic diagnosis of lung cancer is an important, real-world medical problem.in this paper the author has shown how neural networks are used in actual clinical diagnosis of lung cancer.neural network model, a diagnostic system that performs at an accuracy level is constructed here. In this work, the performance of neural network structure was investigated for lung cancer diagnosis problem. People can be checked for lung cancer disease quickly and painlessly and thus detecting the disease at an early stage. Of course, They would become perfect doctors in a box, assisting or surpassing clinicians with tasks like diagnosis. This work indicates that neural network can be effectively used for lung cancer diagnosis to help oncologists. The prediction could help doctor to plan for a better medication and provide the patient with early diagnosis. 81

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