EXPERT SYSTEM ON DIAGNOSIS AND TREATMENT OF DIABETICS

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CHAPTER ONE

INTRODUCTION

  1. INTRODUCTION

During the recent decades, expert systems have been used in some fields including medicine in the developed countries. Yet, in some of the medical areas, major activity for using of expert systems in diagnosis and treatment of related disease, teaching to medical students, advising to patients have not been done. This problem causes spending too much time and money, lack of timely access to physicians, and finally jeopardizing human lives.

In fact, the medical field was one of the first testing grounds for Expert System (ES) technology. MYCIN, NURS Expert, CENTAUR, DIAGNOSER, MEDI and GUIDON, MEDICS, and DiagFH are few of the first and successful medical Expert System.

An Expert System is a computer program that attempts to imitate the reasoning process and knowledge of experts in solving specific types of problems (Garcia et al., 2001).Garcia indicates that Jackson believes that an Expert System is a computer program that represents and reasons with knowledge of some specialist subject with a view to solving problems or giving advice.

Today, based on the statistics of International Federation of Diabetes, there are 230 millions of diabetics through the world, at present time which 80 percent of them are living in the developing countries. Up to 2025, number of diabetics will reach to 380 million (Ghaffari, 2009). Rajab, director of Iran Diabetes Association indicates that yearly about one billion US dollars is expended in the country because of non-controlling of diabetes (Rajab, 2010). Over 4 millions of Iranians are diabetes in the country and about such a rate are facing to the danger of diabetes (Khosrownia 2010). Iran is located in a district which diabetes epidemic is more than universal statics (Ghaffari, 2009). According to Rajab indication, 1/5 of Iranians are diabetics or facing with diabetes danger. Treatment cost of diabetes type II and its side effect is 24 times of its treatment cost without side effect; While 99% of diabetics have not benefit from a proper control (Rajab, 2010). Omidi, managing director of the Country Charitable Support of Diabetic Association, has announced that based upon the existing statics, in average 10% of Iran inhabitants are diabetics—which means 7 million people. He adds that  “today, the most epidemic factor of disabilities in the country such as blindness, amputation, kidney degeneration and sex disabilities are side effects of diabetes (Omidi, 2010).Lee, et al. have presented a system consists of a network system to collect data and a sensor module which measures pulse, blood pressure and so on. They have proposed an expert system using back-propagation to support the diagnosis of citizens in U-health system (Lee et al. 2012). Chen, et al. have represented a three- stage expert system based on support vector machines for thyroid disease diagnosis. They have tried to focus on feature selection, the first stage aims at construction diverse feature subsets with different discriminative capability. In the second stage, the proposed system was used for training an optimal predictor model. Finally, the obtained optimal SVM model proceeded to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. They believed that theproposed FS-PSO-SVM expert system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance (Chen et al, 2011).

Keles, Keles and Yavuz have developed an expert system so called as an Ex-DBC (ExpertSystem for Diagnosis of Breast Cancer). They indicated that the fuzzy rules which will be used in inference engine of Ex-DBC system were found by using neuro- fuzzy method. Ex- DBC can be used as a strong diagnostic tool with 97% specificity, 76% sensitivity, 96% positive and 81% negative predictive values for diagnosing of breast cancer. In addition, they asserted that by means of that system can be prevented unnecessary biopsy (Keles, Keles and Ugur, 2011). Adeli and Neshat have tried to design a system with 13 input fields and one output field. Input fields were chest pain type, blood pressure, cholesterol, resting blood sugar, maximum heart rate, resting electrocardiography (ECG), exercise, old peak, thallium scan, sex and age. The results obtained from their designed system were compared with the data in upon database and observed results of designed system were correct in 94%. The system coded with MATLAB software (Adeli, Neshat, 2010) Zarandi, et al. have designed a fuzzy rule-based expert system for diagnosing asthma. They assert that a knowledge representation of the system was provided from a high level, base on patient perception, and organized into two different structures called Type A and Type B. Type A is composed of 6 modules, including symptoms, allergic rhinitis, genetic factors, symptom hyper-responsiveness, medical factors and environmental factors. Type B was composed of 8 modules, including symptoms, allergic rhinitis, genetic factors, response totests, PEF tests and exhaled nitric oxide. They concluded that the final results of every system are de-fuzzyfied in order to provide the assessment of the possibility of asthma for the patient(Fazel-Zarandi et al. 2010). Singh, et al. have presented an expert system design and analysis for breast cancer diagnosis. The algorithm for rule-based reasoning was addressed which was developed for mammographic findings to provide support for the clinical decision to perform biopsy of the breast. The designed system was evaluated using a round-robin sampling scheme and performed with an area under the receiver operating characteristic curve of 0.83, comparable with the performance of a neural network model (Singh, et al. 2010).

Zolnoori, Fazel-Zarandi and Moin have developed a fuzzy rule-base expert system for evaluation possibility of fatal asthma. Fuzzy-rules, modular representation of variables in regard to patients’ perception of the disease, and minimizing the need for laboratory data were the most important features of the system main variables of viral infections. Evaluating the performance of the system at asthma, allergy, immunology research center of Imam Khomeini Hospital reinforced the good efficiency of that fuzzy expert system for prediction of possibility of fatal asthma (Zolnoori, et al. 2010). Akter, Sharif-Uddin, and Aminul-Haque have provided a knowledge-based system for diagnosis and management of diabetes mellitus.

They believed that preventive care helps in controlling the severity of chronic disease of diabetes. In addition, preventive measures require proper educational awareness and routine health checks. The main purpose of this project was developing a low-cost automated knowledge-based system with easy computer interface. This system performs the diagnostic tasks using rules achieved from medical doctors on the basis of patients’ data.

  1. STATEMENT OF THE PROBLEM

Life is difficult to diabetic patients. They must measure their glucose rate, inject insulin regularly, visit physician and examine the results. An expert system may help them to minimize measurement time for detecting glucose level. By this way, insulin dosage may be planned effectively. Once diabetes expert systems that are already used are examined, we saw that they are not web-based application, but founded in 2-tier architecture. Then they carry all disadvantaged of 2-tier architecture, such as difficulties in maintenance, difficulties in upgrade, limited access (you must have enough capacity on workstations) and so on. Another problem that must be solved by a diabetes expert system is the diabetic map of Turkey. It’s needed to work on data mining with a knowledge based of diabetic patients. However the knowledge base must be pre-processed before applying data mining techniques. The diabetes expert system should solve the pre-processing problem and apply data mining techniques such as classification and association rules and apply Neuro-Fuzzy Inference System like ANFIS (Adaptive Neuro-Fuzzy Inference System) (Polat & Gunes 2006).

  1. AIMS AND OBJECTIVES

The aim of the study is to design an expert system on Diagnosis and treatment of diabetics, however it will achieve the following objectives: –

  • To evaluate the risk analysis for various diabetic’s diseases for the patient using predefined formulas.
  • To recommend the appropriate tests required to be completed prior to a visit.
  • To design a user centric diabetics expert system.
  • To implement the decision of the doctor to prescribe one of several classes of medications.

Evaluate a risk analysis for various Cardiovascular

  1. SCOPE OF THE STUDY

The scope of the study is to design an expert system on Diagnosis and treatment of diabetics.

  1. JUSTIFICATION OF THE STUDY

It will help to reduce death raised caused by diabetic’s patient, raise consciousness and be useful to both government and private individuals. It can also be implemented in all general and private hospitals.

  1. METHODOLOGY

The expert system will be developed following stages

A) Knowledge Acquisition

The knowledge acquisition stage was done through direct in­terviewing with the medical specialists and nurses in the field of diabetes, and studying the related scientific resources

B) Knowledge Representation

The proposed system is rule-based; therefore, for knowledge representation, we used rules in the form of IF…THEN, where IF identifies the situation, and THEN provides the suggestion. Transmission of the experts’ knowledge into these rules has been carried out by using 1) Block Diagram, 2) Mockler Charts, and 3) De­cision Tables.

C) Coding

This expert system has been coded using ASP.NETl, which is a specific tool for coding ex­pert systems.

  1. DEFINITION OF TERMS

AMYLIN: A hormone released along with insulin from beta cells, which decreases glucose levels during meals.

ARTIFICIAL SWEETENERS: Sugar substitutes that taste sweet, have no carbohydrates and essentially no calories, and will not raise blood sugars.

BASAL INFUSION PROFILE: An insulin pump term that refers to the amount of insulin delivered every half hour or hour over a 24-hour period to provide background (i.e., overnight, fasting and between-meal) insulin replacement.

BASAL INSULIN REPLACEMENT: Background insulin replacement, which is the amount of insulin required to stabilize the blood sugar overnight, while fasting and between meals.

BETA CELLS:Specialized cells, which make and release the hormone insulin, that are found in the islets of Langerhans in the pancreas.

BLOOD GLUCOSE: The main sugar that is the body’s source of fuel. Glucose is carried through the bloodstream to provide energy to all cells in the body.

BLOOD SUGAR REBOUND: The hormonal reaction of the body to a low blood sugar (hypoglycemia) that results in high blood sugar – also known as the Somogyi effect.

BOLUS INFUSION: This is an insulin pump term referring to insulin delivery for food or to correct a high blood sugar. The bolus can be given immediately, as an extended bolus – a stable, continuous infusion over an assigned period of time – or as a dual delivery, a designated percentage delivered immediately, with the remainder as an extended bolus.

CALORIE: A unit of measurement that represents the amount of energy provided by food. Carbohydrates and protein provide about 4 calories per gram, while fat yields about 9 calories per gram.

CARBOHYDRATE: One of the three nutrients that supply energy. Carbohydrate is sugar – either single sugars or chains of sugars strung together.

CARBOHYDRATE EXCHANGE LISTS/EXCHANGE LISTS: Foods with a similar amount of carbohydrate, protein and fat calories per serving size are grouped or listed together. The foods within each list can be “exchanged” for one another during meal planning to provide balanced nutrition and to facilitate carbohydrate counting.

CHOLESTEROL: A fat made by the body and consumed in food products that come from animals. Primarily, it travels in the blood as two compounds: low-density lipoproteins (LDL) and high-density lipoproteins (HDL). Chemically, cholesterol is a sterol, or rings of carbons attached together.

CONTINUOUS GLUCOSE SENSORS: Sensors that continuously measure glucose level in the fluid between cells (interstitial fluid.) The average glucose level is displayed on a monitor.

CONTINUOUS SUBCUTANEOUS INSULIN INSULIN INFUSION (CSII): ‘;;’Insulin delivery through an insulin pump.

CORTISOL: A steroid hormone that increases blood sugar by making fats and muscles more resistant to insulin. Cortisol levels may be increased during times of stress. Cortisol is sometimes used as a medicine.

COUNTING CARBOHYDRATES: A method of meal planning for people with diabetes, based on counting the number of grams of carbohydrate in food.

DIABETES CONTROL AND COMPLICATIONS TRIAL (DCCT): A major study of type 1 diabetes conducted in the 1980s that shows the benefits of intensive therapy.

DIABETES/DIABETES MELLITUS: Often known as just diabetes. The sugars are high because there is not enough insulin or because insulin is not effective. The most common types of diabetes are type 1 diabetes and type 2 diabetes.DIABETIC KETOACIDOSIS (DKA): This is a medical emergency caused by not enough insulin. Without insulin, the body will break down fat and muscles for energy and make ketones. Signs of DKA are nausea and vomiting, stomach pain, fruity breath odor and rapid breathing. Untreated DKA can lead to coma and death.

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