AUTOMATED MEDICAL DIAGNOSIS AND THERAPHY SYSTEM

CHAPTER ONE

INTRODUTION

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, MedFrame / CADIAG-IV, FuzzyTempToxopert 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.

1.3 Aim and Objectives of the Study

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:

i. Develop a system which diagnoses the presence of malaria in a patient using pre-defined symptoms.

ii. If detected, the system should provide the severity of the disease, give necessary therapy and give precautions when necessary.

iii. The system should capture and store patient data, diagnosis and therapy history.

iv. The system should serve as a knowledge bank for malaria diseases and provide timely and accurate information when needed.

1.4 Scope and Limitations of the Study

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 dat

Get this material NGN 5,000

Message Us on WhatsApp  »