Fuzzy Rule based Expert System for Diagnosis of Lung Cancer

ijcsit.com

In this paper we design a fuzzy rule based medical model to detect and diagnose lung cancer. The disease is determined by using a rule base, populated by rules made for different types of lung cancer. The algorithm uses the output of the rule base (i.e. the disease name) and the symptoms entered by the user; it also uses the priority and severity values to determine the stage of cancer the patient is in. Both these results (disease name and stage) help the diagnostic logic to determine the treatment for the patient with accuracy. Our medical diagnosis deals with a complex analysis of all the information gathered about our symptoms. Domain expert's knowledge is gathered to generate rules and stored in the rule base and the rules are fired when there exist appropriate symptoms. The system is implemented for the medical diagnosis and treatment for the patients as well as it can be used to assist the doctors.

… sciences: the official journal of the …, 2000

This paper summarizes our research on developing a supporting expert system for pulmonary tuberculosis and lung-disease diagnosis, which has a fuzzy reasoning engine and a fuzzy-rule knowledge-base. The system has undergone through the theoretical and practical ...

Computer Methods and Programs in Biomedicine

Background and objectives: A Medical Expert System (MES) was developed which uses Reduced Rule Base to diagnose cancer risk according to the symptoms in an individual. A total of 13 symptoms were used. With the new MES, the reduced rules are controlled instead of all possibilities (2 13 = 8192 different possibilities occur). By controlling reduced rules, results are found more quickly. The method of twolevel simplification of Boolean functions was used to obtain Reduced Rule Base. Thanks to the developed application with the number of dynamic inputs and outputs on different platforms, anyone can easily test their own cancer easily. Methods: More accurate results were obtained considering all the possibilities related to cancer. Thirteen different risk factors were determined to determine the type of cancer. The truth table produced in our study has 13 inputs and 4 outputs. The Boolean Function Minimization method is used to obtain less situations by simplifying logical functions. Diagnosis of cancer quickly thanks to control of the simplified 4 output functions. Results: Diagnosis made with the 4 output values obtained using Reduced Rule Base was found to be quicker than diagnosis made by screening all 2 13 = 8192 possibilities. With the improved MES, more probabilities were added to the process and more accurate diagnostic results were obtained. As a result of the simplification process in breast and renal cancer diagnosis 100% diagnosis speed gain, in cervical cancer and lung cancer diagnosis rate gain of 99% was obtained. Conclusions: With Boolean function minimization, less number of rules is evaluated instead of evaluating a large number of rules. Reducing the number of rules allows the designed system to work more efficiently and to save time, and facilitates to transfer the rules to the designed Expert systems. Interfaces were developed in different software platforms to enable users to test the accuracy of the application. Any one is able to diagnose the cancer itself using determinative risk factors. Thereby likely to beat the cancer with early diagnosis.

2016

Traditionally human experts were responsible for taking decisions in solving the medical problems. But it was very difficult for human expert to solve complex problems,for that expert systems were designed.There are lot of applications in artificial intelligence domain that try to help human experts offering solutions for a problem. Expert system is a part of artificial intelligence which increases the ability of decision making of the human expert. They are designed to solve complex problems.Rule based expert system uses rules as the knowledge representation for knowledge coded into the system. Rule based expert system is used by the human experts to diagnose the problem.This paper surveys research work accomplished in the field of medical sector using rule based expert system.

Advances in Intelligent Systems and Computing, 2019

According to the World Health Organization (WHO), human disease results in at least 70% of deaths every year. Approximately, 56 million people died in 2012 and 68% of all deaths in 2012 were as a result of non-communicable diseases. The aim of this paper is to design and develop a web-based fuzzy expert system that would diagnose some of these diseases and provide users with expert advice and prescriptions based on the diagnosis generated by the system. The system would not only indicate if the disease is present but will also indicate the level at which the disease is present. The system is designed to diagnose five diseases which include asthma, diabetes, hypertension, malaria and tuberculosis. The system uses Mamdani inference method which has four phases: fuzzification, rule evaluation, rule aggregation and defuzzification. The fuzzy expert system was designed based on clinical observations and the expert knowledge. Having performed the experimentation and obtained relevant resu...

The aim of this study is to design a fuzzy expert system for calculating the health risk level of a patient. The fuzzy logic system is a simple, rule-based system and can be used to monitor biological systems that would be difficult or impossible to model with simple, linear mathematics. The designed system is based on the modified early warning score (MEWS).The system has 5 input field and 1 output field. The input fields are blood pressure, pulse rate, SPO2 (it is an estimation of the oxygen saturation level in blood.), temperature, and blood sugar. The output field refers the risk level of the patient. The output ranges from 0 to 14. This system uses Mamdani inference method. A larger value of output refers to greater degree of illness of the patient. This paper describes research results in the development of a fuzzy driven system to determine the risk levels of health for the patients. The implementation and simulation of the system is done using MATLAB fuzzy tool box.

Fuzzy Expert Systems for Disease Diagnosis, 2015

The incidence of breast cancer is increasing day by day. Emotional significance of females for the fear of removal of breast demands attention and carries a particular terror. Fuzzy logic-based expert system is a powerful tool that is used in this chapter to get the benefits of soft computing in modern medical science. This chapter deals with reasoning for medical implementation in breast cancer diagnosis. The motto of this expert system using MATLAB software is to make the people of the world healthier, free from breast cancer and its metastasis through the power of information. Special revolutionary screening technology and a few (cancer markers) blood tests enable breast cancer diagnosis, configuration, and control, and prompt necessary decisions for treatment. Thus, this system provides healthier living, better healthcare outcomes, and helps to lower the overall cost of the healthcare system.

International Journal of Applied Mathematics, Electronics and Computers, 2014

This paper is the survey of studies that include design processes of some fuzzy expert systems for applications in some medical area. Recent studies of us that include fuzzy expert systems that make use of fuzzy logic method were described. Some of fuzzy expert systems mentioned in this paper have been developed as first time. Followings were investigated; the risk of prostate cancer, risk of coronary heart disease, degree of child anemia, determination the level of iron deficiency anemia, diagnosis of periodontal dental disease, determination of drug dose and etc. All designed fuzzy expert systems can help in support decision process of physicians. It can be claimed that in many cases such systems can help the physicians in diagnostics, treatment of illness, patient pursuit, prediction of disease risk and etc. As can be seen from the paper accuracies ratios of the proposed FESs were find as high by doctors involved to studies. Robustness and reliabilities of the developed FESs were proved on patients by doctors. The numbers and other arguments that support our claim can be found in the paper.

2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)

Non small cell Lung cancer (NSCLC) is one of the most well-known types of Lung cancer which is reason for cancer related demise in Bangladesh. The early detection stage of NSCLC is required for improving the survival rate by taking proper decision for surgery and radiotherapy. The most common factors for staging NSCLC are age, tumor size, lymph node distance, Metastasis and Co morbidity. Moreover, physicians' diagnosis is unable to give more reliable outcome due to some uncertainty such as ignorance, incompleteness, vagueness, randomness, imprecision. Belief Rule Base Expert System (BRBES) is fit to deal with above mentioned uncertainty by applying both Belief Rule base and Evidential Reasoning approach .Therefore, this paper represents the architecture, development and interface for staging NSCLC by incorporating belief rule base as well as evidential reasoning with the capability of handling uncertainty. At last, a comparative analysis is added which indicate that the outcomes of proposed expert system is more reliable and efficient than the outcomes generated from traditional human expert as well as Support Vector Machine (SVM) or Fuzzy Rule Base Expert System (FRBES).

Medical Science and Discovery

Objective: Our aim is to develop a medical expert system for pulmonary diseases providing practitioners and medical students with the advantages of improving their ability, minimizing the error and cost in diagnosing and developing their medical knowledge. Material and Methods: CLIPS has been chosen as a programming environment for this study. A respiratory disease binary decision tree which helps us to create the system database which includes twenty-eight diseases is formed for the inference engine of this program. Results: The evaluation of this program is based on hundred and eighty-nine patients' data each is classified into three data types. These are patient's history and physical findings, radiological data and laboratory data. The combination of them shapes four different data sets for each patient. The diagnosing result for each data set of each patient is compared with diagnosing of gold standards. If both results indicates the same disease this operation of the program is assumed as "accurate", otherwise as "error". These operations for the considered each data set are repeated for all patients' data. The total number of accurate diagnosis is divided by the number of all patients and these accuracy rates are respectively 64.02%, 71.43%, 82.54% and 96.83%. Conclusion: We can conclude that the accuracy of the system is enhanced with the increasing total number and type of data for each patient. Finally, further improvement on the performance and accuracy of the system may be obtained by designing the program with the self-learning ability.