ABSTRACT
The use of Artificial Intelligence in medical diagnosis is becoming increasingly common and has been used widely in the diagnosis of cancers, tumors, diabetes, hepatitis, lung diseases, etc. There is a growing interest in the use of computer-based Clinical Decision Support Systems (CDSSs) to reduce medical errors and to increase health care quality and efficiency. Diabetes Neuropathy is a chronic health problem with devastating, yet preventable consequences.Due to this shortage of specialists, there is a need for a Clinical Decision Support System that will diagnose and manage diabetes neuropathy.This work therefore aimed at designing a web-based Clinical Decision Support System for the management of early diabetes neuropathy.
Four pattern classification algorithms (K-nearest neighbor, Decision Tree, Decision Stump and Rule Induction) were adopted in this study and were evaluated to choose the most precise algorithm to be employed in the developed clinical decision support system. The evaluation was carried out on appropriate dataset that was obtained from Babcock University Teaching Hospital and the University of Port Harcourt Teaching Hospital. The following benchmarks were used in comparing the generated models: performance, accuracy level, precision, confusion matrices and the models building’s speed.
From the models comparison, the study showed that Naïve Bayes outperformed all other classifiers with accuracy being 60.50%. k-nearest neighbor, Decision Tree, Decision Stump and Rule induction perform well with the lowest accuracy for x- cross validation being 36.50%. Decision Tree falls behind in accuracy, while k-nearest neighbour and Decision Stump maintain accuracy at equilibrium 41.00%. Therefore, Naïve Bayes is adopted as optimal algorithm in the domain of this study. The rules generated from the optimal algorithm (Naïve Bayes) forms the back-end engine of the Clinical Decision Support System.The web-based clinical decision support system was then designed using Adobe Creative Suite 6 as its integrated development environment in which all the web language codes was executed, PhpMyAdmin as the server side scripting language, and MySQL as the database server.
In conclusion, the automatic diagnosis of diabetes neuropathy is an important real-world medical problem. Detection of diabetes neuropathy in its early stages is a key for controlling and managing patients early before the disabling effect present. This system can be used to assist medical programs especially in geographically remote areas where experthuman diagnosis not possible with an advantage of minimal expenses and faster results. For further studies, researchers can improve on the proposed clinical decision support system by employing more than one efficient algorithm to develop a hybrid system.
Keywords: Diabetes, Neuropathy, Precision, Classification, Algorithm, Accuracy, Clinical.
CHAPTER ONE
INTRODUCTION
1.1.Background to the Study
The world is fast evolving and in order to cope with the insatiable demand of the human race for the kind of living that can be described as top-notch in which people have all they need at their beck and call, there is the need to develop intelligent decision making applications that will drive systems or devices to carry out tasks that require human intelligence. This concept is known as Artificial Intelligence (AI).In science and technology, the desire for improvement is a constant subject which triggersadvancements.Technology has changed civilization in many different ways. Humans have always been on a path of progression through the help of technology, the twentieth and twenty-first centuries have seen a number of advancements that revolutionized the way people work, live and play.
Artificial Intelligence (AI) is the area of Computer Science focusing on creating expert machines that can engage on behaviours that humans consider intelligent. Artificial Intelligence is the branch of Computer Science that is concerned with the design and development of the intelligent systems. Recent advances in the field of Artificial Intelligence have led to the emergence of expert systems and computational tools; designed to capture and make available the knowledge of experts in a field.Expert system is an area of Artificial Intelligence that emulates the decision-making ability of a human expert (Jackson, 1998). Expert systems are designed to solve complex problems by reasoning about knowledge, represented primarily as if–then rules rather than through conventional procedural code.The use of computer technology in areas of diagnosis, treatment of illnesses and patient pursuit has highly increased. Though, the fields in which computers are being used have very high complexity and uncertainty; the uses of intelligent systems such as fuzzy logic, artificial neural network and genetic algorithm have been developed (Jimoh et al, 2014).
A Clinical Decision Support System (CDSS) is an active knowledge system, where two or more items of patient data are used to generate case-specific recommendation(s) (Chen et al, 2002). This implies that a CDSS is a Decision Support System (DSS) that uses knowledge management to achieve clinical advice for patient care based on some number of items of patient data. This helps to ease the job of healthcare practitioners, especially in areas where the number of patients is overwhelming. Clinical decision support system (CDSS) provides clinicians, staff, patients or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care. A CDSS can also be seen as an application that analyses data to help healthcare providers make clinical decisions (Rouse, 2014).
