Other Diseases Detection

Considering the importance of the problem of medical diagnosis, this chapter investigates the application of an intelligent system based on artificial neural network for decision making for Hepatitis. First, datasets are provided for detecting Hepatitis, based on the requirements of artificial neural network inputs and outputs consisting of associated symptoms of each disease as fields of patients' records. Then multilayer perceptron (MLP) artificial neural network is trained to classify Hepatitis disease. In the next sections, details are described.

Intelligent Detection of Heart Disorders

In this chapter, the first stage for detecting heart disorders (that is, noise removal) is explained. Two intelligent approaches based on Self Organizing Map (SOM) and Particle swarm Optimization (PSO) are used to train the feedforward neural network for noise removal. The trained ANNs are used to find the cutoff frequency. Then the found cutoff frequency is applied by a bandpass FIR filter for ECG noise removal.

Artificial Intelligence in Medical Science

Applying Artificial Intelligence (AI) for increasing the reliability of medical decision making has been studied for some years, and many researchers have studied in this area. In this chapter, AI is defined and the reason of its importance in medical diagnosis is explained. Various applications of AI in medical diagnosis such as signal processing and image processing are provided. Expert system is defined and it is mentioned that the basic components of an expert system are a “knowledge base” or KB and an “inference engine”. The information in the KB is obtained by interviewing people who are experts in the area in question.

Procedure of Medical Diagnosis

Drawing flow diagrams is an effective strategy to extract rules for designing an intelligent system. Physicians diagnose the diseases based on flow diagrams. In this chapter, the procedure and required steps for medical diagnosis are explained and the reader can learn the way to find the knowledge from the medical experts to extract the data for the intelligent system. Examples of some flowcharts from the California Department of Health Service are provided to show how the designer should work with the medical data. Medical data and types of patient information are described.

Medical Diagnosis

Medical diagnosis has some difficulties that cause the physician mistakes during the process. Many of the difficulties are related to the vast amount of medical data, similarity of the symptoms for many of the diseases and physician skills and experiences. This chapter briefly explains the medical diagnosis definition and the useful techniques that help to improve the performance of the existing medical diagnosis systems. The reasons for importance and difficulties of medical diagnosis and web based medical diagnosis system components are explained and WISER as an example is provided.

ANN Optimization Experiments for Classification

This chapter shows the application of PSO and GA algorithms for training the neural network using two datasets: XOR and Iris. Then the performance of both algorithms are compared and presented by figures. In addition, some of the other optimization algorithms such as Gravitation Search Algorithm (GSA) and Ant Colony Optimization (ACO) are explained.

Feature Extraction and Classification

In the previous chapter, the first stage for detecting the ECG noise removal was investigated. In this chapter, the second and the third stages are explained. The Second stage is to extract the effective features of the ECG signals. The final stage is to use MLP and PSO algorithms for classification of ECG signals to detect the 4 common heart disorders including the normal signals. Common disorders are Normal, Supraventricular, Brunch bundle block, Anterior myocardial infarction (Anterior MI), and Interior myocardial infarction (Interior MI).

Review on Heart Studies

For many years, researchers have studied various aspects of heart disorder detecting. Noise removal, feature extraction and optimized approaches for classifications of heart signals are some of the main areas of their studies. In this chapter, some of the previous research on the mentioned areas is collected so that the readers may form a view from the total process of heart disorder detecting.

Introduction to Heart

The heart is an important organ in the human body, for pumping the blood throughout the body. An electrocardiogram (ECG) is a diagnosis tool that reports the electrical operation of the heart, recorded by skin electrodes at specific locations on the body. The introduction of computer-based methods for the purpose of quantifying different ECG signal characteristics is the result of a desire to improve measurement accuracy as well as reproducibility. In this chapter, the author explains the basic definitions in heart studies and the electrocardiogram signals. In addition, the importance of interpretation and measuring the effective features in heart signals to detect the heart disorders is described. Finally, some of the common disorders of heart are briefly explained.

Artificial Neural Network for Medical Diagnosis

This chapter mentions AI which has various applications in medical diagnosis. One of the most impressive processing tools in this area is the Artificial Neural Network (ANN) that has improved the performance of the existing diagnosis systems. ANN as one of the advanced intelligent tools for medical diagnosis is a subject of researching for finding the algorithms for better medical diagnosis. Applications of ANN in pattern recognition, drug development and medical diagnosis such as hepatitis, cancer and heart diagnosis is wildly investigated by researchers. In this chapter, a brief explanation of ANN for diagnosis of same diseases is provided.