Early detection of Heart Diseases using ANN

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chandraveer@icloud.com. Prof. Dr. Aynur Unal. Founder of DIGITAL .... security is a major concern in communication systems but with the help of Artificial Neural ...
Early detection of Heart Diseases using ANN (Time and Cost Efficient) Chandraveer Singh Rathore* School of Engineering and Technology Ansal University Gurgaon, India [email protected]

Shivaen Katial School of Engineering and Technology Ansal University Gurgaon, India [email protected]

Prof. Dr. Aynur Unal Founder of DIGITAL MONOZUKURI http://dm.amidstky.com/ California (CA), USA [email protected]

Antimdev Mishra Assistant Professor School of Engineering and Technology Ansal Unversity Gurgaon, India [email protected]

Prof. Jay Rajasekera Professor International University of Japan http://www.iuj.ac.jp/faculty/jrr/ [email protected] Saurabh Juneja School of Engineering and Technology Ansal University Gurgaon, India [email protected]

Abstract — Artificial Neural Networks (ANN) have

the ability to exploit the resistance for imprecision and ambiguity in real-world- problems, and their robustness and uniformity; ANN and their techniques have become increasingly important for modelling and development in many areas of science and engineering. This paper presents an analytical overview of the most popular ANNs. After an overview of ANN, the paper discusses global expansion for ANN training. The paper then discusses the techniques and means for detecting heart diseases at an early stage at almost negligible costs and lesser time. Keywords: ANNs, heart diseases, early detection, cheaper costs, time efficient, backpropagation, review

I. INTRODUCTION

In today’s fast life, unhealthy diet, obesity, high blood pressure and physical inactivity have increased the risk of heart attacks. According to a WHO report, an

estimated count of 17.3 million people died from heart attacks in 2008 (30% of all global deaths). And, the number of people who die from heart attacks will increase to reach 23.3 million by 2030. In present times, only rich people have access to early detection of heart diseases. WHAT IS ANN?

Neural networks and artificial intelligence (AI) a r e suitable for vague problems, such as recognition, prognosis, control and classification. A neural network consists of processing units, called ‘neurons’ or ‘nodes’. It bears a passing similarity to actual biological neurons. A neuron is interconnected in the network by unidirectional connections with different strengths and weights. In earlier times, designs were positioned on perceptrons, which are capable of altering elements which computed their entire inputs and applied a linear transfer function to generate an output. In present times, networks use either binary or continuous activations, or linear and non-linear functions.

For a network to be trained using a supervised learning system a ‘training data set’ of sample inputs and their corresponding desired outputs are required. The network weights, which will finally store the learnt patterns, are initially set to small random values. During learning, the example inputs are conferred to the network and the resultant and desired outputs are set side by side. In general, an ANN can be viewed as a system that generates a desired response to an input stimulus. The pattern of connectivity in an ANN (that is the strengths of the connections between various processing units) defines the causal relations between the network's processors, and is therefore corresponding to a program in a conventional computer. However, in contrast to a conventional computer, the ANN is not given a step by step procedure to perform some desired task. Instead, the network is taught to do the task. In loose words, ANN is a biomimicry of a human brain. II. LITERATURE SURVEY

Biological neurons are architecturally more complex than the simplistic description. They are somewhat more complex than the existing artificial neurons that are built into today’s artificial neural networks. Processing units are normally considered as being corresponding to neurons, and are assumed to work in parallel. The nature of a single processing unit in an ANN can be represented as follows:  First, the unit estimates the total signal being sent to it by other processors in the network.  Second, the unit implements an activation function to the total signal, in order to adopt a particular level of internal activity.  Third, the unit sends a signal to other processors in the network; this signal is a function of the unit's internal activity. The signal that one processor sends to another is transmitted through a packed connection, which is generally described as being corresponding to a synapse. WHAT IS BACKPROPAGATION?

One example of supervised learning is backpropagation, a common method of practicing a neural net in which the basic system output is compared to the desired output, and the system is fine-tuned until the difference between the two is diminished.

It will be dealt in some depth here as many of the applications use this concept. Using the back propagation algorithm, it is possible to train networks containing hidden layers and so implement complex non-linear mappings between the input and output domains, as opposed to the simple linear mappings possible with perceptron networks. It requires a known and desired output for each input value for the calculation of the loss function gradient. It is usually considered as an administered learning method although it is also used in some unsupervised networks like autoencoders. It also requires that the stimulating function used by the artificial neurons or nodes be differentiable. The delta rule (a learning rule for updating weights) is categorized into multi- layered feed forward networks, which is made possible by using the chain rule to iteratively compute gradients with each layer. As referred from the Neuroph studio, the aim is prediction and classification of severe and chronic diseases. This will give an idea of the problem and the 'disease ratio' which will reduce a doctor's effort and would take much lesser time and costs. It is an attempt to simulate within specialized hardware or sophisticated software, the multiple layers of simple processing elements called neurons. Each neuron is linked to its neighbours with varying coefficients of connectivity that represent the strengths of these connections. Learning can be done by adjusting these strengths to cause the overall network to output convenient results. Softwares which were taken into consideration were NetBeans, Weka and Neuroph Studio Java is a high level programming language. It has a number of feature that make the language apt for use on World Wide Web. It was also used in the compilation of the main program. NetBeans is an open source integrated development environment (IDE) for developing, basically with Java. I All Java application types are supported by NetBeans. Weka is an open source software which was developed by University of Wekato, New Zealand. Neuroph is lightweight Java neural network framework to develop simple neural network designs. It is available under Apache 2.0 license and is free to use. Neuroph Studio also supports image recognition, handwritten letter recognition and text character recognition.

And the rest of the data for No.7 to No.15 was taken from Gautam Hospital, Adarsh Nagar, New Delhi. The hospital requested not to disclose the identity of these nine people; thus name shown here are fictitious. IV. ANALYSIS The ANN model in Figure 1.1 was created in Weka and the program was compiled in NetBeans. The scale mentioned here, was consulted from a hospital and a pathological lab.

Figure 1.1. Classifier that uses backpropagation to classify seven attributes

III. EXPERIMENTATION

The aim is to minimize the effort of the doctors and detect potential heart diseases efficiently and in a cost efficient manner. The real time data was used to monitor the attributes of the patients such as Age, Sex, Haemoglobin(Hb) level, Total cholesterol, HDL Cholesterol (Good Cholesterol), LDL Cholesterol (Bad Cholesterol), Heart Beat Rate. These attributes were compared with the optimal and ideal values so as to monitor the gravity of the situation. Usually, Haemoglobin(Hb) level is optimal between 13-18 mg/dl; below or greater reading will result in different symptoms. As and when the values of the patients are entered in the program, the program matches the values with the optimal ranges and displays the result accordingly and also specifies the Disease Ratio of the person according to the variations in the respective attributes and specified ranges. Table 1.1 is drawn to feature the attributes of the patient. The optimal ranges have been specified. The output window was made using NetBeans IDE 8.0. The values are entered comparing attributes from Table 1.1. After entering the values, the comments block shows the optimal ranges of the particular attribute and in the end gives the disease ratio of the risk of the disease. Two screenshots of the program have been accommodated for convenience as Figure 1.2 and Figure 1.3. The data for No.1 to No.6 in Table 1.1 was taken from Dr Lal Path Labs, Mukherjee Nagar, New Delhi. And the patients are 1. Harsh Grover 2. Surendra Mohan 3. Sajal Katial 4. Sudha Rani 5. Namrata Sethi 6. Bharat Bhushan

Table 1.1. Real time data for 15 patients with 7 attributes

Scale: Haemoglobin(Hb) Level Optimal Range- 13-18 mg/dl < 13 mg/dl - Low Hemoglobin Level > 18 mg/dl - Above Optimal/ Borderline High Level Total Cholesterol Optimal Range- < 200 mg/dl 200 - 239 mg/dl - Borderline High Level >= 240 mg/dl High Level HDL Cholesterol Optimal Range - 40 - 60 mg/dl LDL Cholesterol Optimal Range - < 100 mg/dl 100 - 129 - Above Optimal 130 - 159 Borderline High 160 - 189 High

planned and validated before actual testing started. Accurate details of implemented neural network topology were acknowledged. In this project, Artificial Neural Network technique allowed us to successfully collect information and compete with other complex machines in order to obtain specific results. The efficiency of the program was in the range of 90-92%. The goal of this project is reducing the costs and increasing the efficient at the same time. Sometimes data security is a major concern in communication systems but with the help of Artificial Neural Networks we can overcome this problem very quickly. Few demerits were highlighted during the whole research. The whole process is too complex and just cannot be understood by a layman. In the future, we wish to make this process as easy as testing sugar for diabetes. VI. FUTURE SCOPE

Figure 1.2. JAVA model for Experiment 1

Figure 1.3. JAVA model for Experiment 2 V.

CONCLUSION

Much attention has been given to the operation of ANN model to obtain results. Testing of model has ensured that it was working properly and was systematically

Most practical applications of artificial neural networks are based on a computational model involving the propagation of continuous variables from one processing unit to the next. As far as future is concerned, it is believed there are several areas yet to be explored by Artificial Neural Networks or some other great models believed to be developed soon which can give us good and more effective results and error free reports which user expect in today’s world. The most common use for neural networks is to project what is most likely to happen. There are many areas where prediction can help in setting priorities. For example, the emergency room at a hospital can be a hectic place, to know who needs the most critical help can enable a more successful operation This method will reduce the doctor’s effort to check the reports and nature of the disease. It will take lesser time to figure out the disease as compared to any conventional report. It is cheaper and time efficient. It is believed this method can be extended for an application of a power system, automobile engineering, application screening for jobs, control of a wireless car, monitoring of stock market on which whole world’s economy depends upon and much more. ACKNOWLEDGEMENT

We would specially like to thank Dr. Aynur Unal (Stanford Alumni) for her immense contribution to this paper. She has been into the research field since decades and has published many technical papers. This paper was completed under the undivided guidance of her.

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