The Design And Implementation Of An Automated Medical Diagnosis And Therapy System (PDF/DOC)
The unavailability of medical experts in the developing world has subjected a large percentage of its populace to preventable ailments and deaths. Also, because of the predominant rural communities, the few medical experts that are available always opt for practice in the few urban cities. The aim of this project work is to take a look at the development and the rapid growth of modern technology, especially computer technology in medical fields and how it can serve as a backbone to an automated diagnosis and therapy system. Medical diagnosis and therapy system involves the state of medical knowledge of a particular disease, patient history, examination diagnosis and therapy; it also involves the drug remedy for the patient. It also provides a systematic framework for history taking and physical examination of patient. For implementation purposes, we used malaria as a case study.
This study therefore models an algorithm for malaria diagnosis and therapy using supervised learning. Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. In this case, a supervised learning technique known as fuzzy logic is used. The fuzzy logic (FL) algorithm for malaria diagnosis and treatment using was designed and simulated using PHP programming language and 100 fuzzy decision matrices, eight symptoms were constructed and used. The developed system proved to facilitate a knowledge bank and a timely recall of accurate information about malaria disease for the purpose of accurate diagnosis and also provides standard procedures for health care delivery and evaluation.
Introduction
1.1 Background Of The Study
Medical care has increased its extent, direction and purpose. It is continually evolving in response to perceive needs and demands. The management of health care delivery and services involves considerable amount of medical records and managerial data, which have to be collected, stored, selectively retrieved, updated and statistically analyzed. With the rapidity growth of modern technology, especially computer technology in all fields of human endeavor, i.e. professionals, scientist, education, industrial and even government activities, e.t.c. necessitate the need for various sectors of the economy to be acquainted with at least basic knowledge of computer application in their day to day operations.
Therefore medical diagnosis and therapy system involves the state of medical knowledge of a particular disease, patient history, patient examination and drugs remedy. The standing orders which are disease symptom, patient history, patient examination, diagnosis and therapy which is called the hierarchical order gives basic health workers legal protection for providing primary health care. They provide a systematic framework for history taking and physical examination of patient. They facilitate a knowledge bank and timely recall of accurate information about specific disease for the purpose of accurate diagnosis and therapy, also to provide standard procedures for health care delivery and evaluation.
Nowadays, medical diagnostic processes are carried out with the aid of computer-related technologies, which are on the increase daily. These systems are mostly based on the principles of artificial intelligence and are designed not just to diagnose based on symptoms but also prescribe treatments based on such. According to [Hegde et al., 2018], “in the medical field, many decision support systems (DSSs) have been designed, such as Aaphelp, Internist I, Mycin, Emycin, Casnet/Glaucoma, Pip, Dxplain, Quick Medical Reference, Isabel, Refiner Series System and PMA which assist medical practitioner in their decisions for diagnosis and treatment of different diseases”.
The proposed Medical Diagnostic System (MDS) is meant to diagnose malaria diseases. Fuzzy logic is chosen as the artificial intelligence tool employed in the proposed system because it is one of most efficient qualitative computational methods. Fuzzy logic has been proven to be one of the effective methods to bring clarity in the world of medicine. In the words of [Esayas, 2020], “it has been used in many areas of medical applications such as in CADIAG, MILORD, DOCTORMOON, TxDENT, Med Frame / CADIAG-IV, Fuzzy Temp Toxopert and MDSS”. Automated systems based on fuzzy logic have been used widely in control systems, household appliances, decision making systems, the medical and automobile industries. While Boolean algebra set values only include {0, 1} or {False, True}, it is believed through fuzzy logic that there are other values between 0 and 1 or False and True, which are sometimes referred to as in-between values. In other words, Boolean logic engages the principles of totally inclusive and exclusive rules on its set of {0, 1} while the principles of totally inclusive, exclusive and ‘in between values’ rules is engaged in fuzzy logic. This intelligent system helps with the screening of malaria patients, thus reducing the workload of practitioners in diagnosing large numbers of patients. It also helps to enhance the accuracy of malaria detection by doctors with little experience in diagnosing this disease.
1.2 Statement of the Problem
One of the major problems that both the developed and under-developed countries are facing is the difficulties in diagnosing and treating of unhealthy people. There is shortage of medical expertise in various hospitals, most of these countries are spending affluence of their resources to meet this challenge but still they are unable to meet the demands of providing good medical services for their people. There arises the need to have an artificial intelligent machine capable of diagnosing a sickness based on series of symptoms.
Malaria also, as other deadly diseases is a threat to human life. Due to its different types and mode of operation, early diagnosing of malaria has been a major problem in an ordinary clinical setting. The traditional method of medical diagnoses where the doctor feels your temperature and asks you a few questions is characterized by inaccuracy and imprecision especially for an inexperienced practitioner. Since artificial intelligence has been proven to be successful in decision making, it has then become of great concern to find a lasting solution to these problems using computer aided means.
Many researchers have developed several algorithms and technologies to ensure accuracy and precision in the field of medicine. Of the entire developed algorithm, we used fuzzy logic. Fuzzy logic is used because it enhances the accuracy and precision of medical diagnosis using array on decisions. The proposed system proffers solution to the enormous responsibilities of the diagnostic process carried out by medical personnel, hence speeding up diagnosis process.
The main aim of this project is to develop a medical diagnosis and therapy system for malaria diagnosis. To achieve the stated aim, the following specific objectives were laid out:
Develop a system which diagnoses the presence of malaria in a patient using pre-defined symptoms.
If detected, the system should provide the severity of the disease, give necessary therapy and give precautions when necessary.
The system should capture and store patient data, diagnosis and therapy history.
The system should serve as a knowledge bank for malaria diseases and provide timely and accurate information when needed.
This research work revolves around the design of a medical diagnosis system using supervised learning. In this project, we use fuzzy logic machine learning algorithm to model the disease learning and diagnosis process. It does this by acquiring deep knowledge of the disease and through a series of symptoms, predicts the disease and its severity. Precautions to have in mind when diagnosed are displayed by the system. Possible treatments and therapy are given when the presence of disease is noticed. The system is designed using PHP programming language and runs on a browser.
1.5 Significance of the Study
The major contribution of this paper is that it applies machine learning to medical diagnostic system. The reason for choosing fuzzy logic is to address uncertainty and vagueness that characterized traditional medical diagnostic practice. The system is tailored to be used in diagnose of malaria. With this, stress on the doctor is reduced and in a case where there is no specialist available this system can be used.
This work will also be of immense help to scholars researching the field of artificial intelligence. It can serve as a reference point to them or a beginning point to other system.
1.6 Project Methodology
Data was needed during the course of this project research. Below are the methods used in collecting data during my research.
i. Interview:
I went to the hospital used as my case study. I did some interview with the head of department of medical diagnosis and therapy system to get some data used in this project.
ii. Browsing:
I browse through the interest to get some information which I need in my project. I browse the medical website (http://www.medical.co.uk)
Collection of data was carried out the hospital. The data was collected using secondary data collection method which involves direct interview of consultants in general medicine. The data collected include various methods for diagnosing malaria and the various signs and symptoms. In addition, previous research works and texts in the subject area will be consulted. These data were put together and using fussy logic and PHP, we implemented an algorithm which successfully diagnosed malaria in patients.
This study is developed under five chapters. The first chapter introduces the research topic, stating the background of the intended project, statement of the problems, project objectives, its significance to the society and overall scope. The second chapter reviews related literature on medical diagnosis system. It analyses previous research works, their limitations and need for the development of better system. The third chapter discusses the methodology used for the project development, the limitations of the currently used system and reasons the intended system should be chosen over the current system. It also showcases the design processes of the new system. Chapter four showcases the actual running of the developed system. Here proper tests are done to check the strength of the developed system. The developed system is analyzed to determine its conformation with the stated objectives. Chapter five gives the summary of the project, gives the conclusion and recommends approaches for better system,.
1.8 Definition of Terms
Therapy System:
This is the healing treatment of the disease identified.
Medical Diagnosis:
This is the identification of a disease or conditions after observing its sign.
Patient Examination:
This is when patient are examined physically and laboratory tests are carried out and recorded.
Diagnosis:
This is the output of the examination recorded.
Therapy:
This is the solution to the suspected disease.
Summary, Conclusion And Recommendation
5.1 Summary
This project highlighted the practical implementation of computer system with view of demonstrating the success and utility of the knowledge base on system in order to gain the practitioners who are the ultimate users of the system. Also, the use of computer assisted techniques lead to more accurate and timely diagnosis and to improve the quality of patient care through reduction in mobility and increase interaction with patient.
Accurate medical parasite diagnosis is one of the major methods to sustain good health and long live. It is a very useful tool in dealing accurate; solve problems that appear to be solved only by human when data is modeled well.
The hospitals will get enough storage media to saving the database of medical reports of patients to serve a patient’s medical history for future. This database can be referenced among health facilities. It provides method of parasite diagnosis with so much accurate and reduces the hours spent by patients in a hospital.
5.2 Conclusion
The experimental study has clearly revealed the role of computer system as an intermediary between the patient and the doctor. The doctor is to examine his patient as in orthodox approach, perhaps, even more thoroughly as he would be under pressure for greater precision. The computer system facilitates fast and accurate information retrieval, which may help to reduce the problem of manual work through of voluminous medical text books. There is also the prospect of standardize and high intelligent differential diagnosis and theraphy procedure.
Apart from prescription and treatments, diagnosis of the ailments remains a primary duty of medical personnel and the creation of an automated diagnostic system assist in decision making and easy and fast discovery of ailment affecting a patient. The adoption of fuzzy logic in the design of such automated system gives an innovative and professional means of malaria diagnosis especially in the part of world where malaria is in wide spread. The use of fuzzy logic in the design of the diagnostic system proposed in this paper is believed to serve as a dependable and cheap means of treating malaria. Furthermore, implementation of Fuzzy logic based medical diagnostic system will reduce doctors’ job during consultation and ease other problems associated with hospital consultations.
5.3 Contribution To Knowledge
The major contribution of this paper is that it applies fuzzy logic algorithm to medical diagnostic system. The reason for choosing fuzzy logic is to address uncertainty and vagueness that characterized traditional medical diagnostic practice. The system is tailored to be used in diagnose of malaria. With this, stress on the doctor is reduced and in a case where there is no specialist available this system can be used. This work will also be of immense help to scholars researching the field of artificial intelligence. It can serve as a reference point to them or a beginning point to other system.
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