Design and Implementation of a Web-based Healthcare Information System for Malaria Diagnosis
Chapter One
OBJECTIVE OF THE STUDY
The aim of this research is to design and implement on web-based health care information system that will aid doctors on the undying struggle to eradicate a disease like malaria.
- To implement a system that will helps in the identification of malaria.
- It give accurate efficient information on the treatment of malaria.
- It give information on the prevention and control of malaria.
- Its almost objective is not to be reliable and to be able to act as doctor in the absence of such doctors.
CHAPTER TWO
LITERATURE REVIEW
INTRODUCTION
Several clinical syndrome now know to be caused by infection malaria parasite were first recognised centuries before the discovery of their pathogens. Consequently, the disease was referred to in terms of their outstanding clinical features, usually the type of febrile cycle.The important role of a hospital in health development of the people and their low level of awareness over health matters gave rise to the institution of hospital health care information system through production of pamphlets and posters for the purpose of educating the people in hospital and clinics.
REVIEW OF RELATED LITERATURES
Web-Based Medical Assistant System for Malaria Diagnosis and Therapy was developed in Adetunmbi et al., (2013). The motivations were that most of the existing systems on malaria diagnosis fail to provide therapy while some provide therapy without diagnosis, half of the world’s population is at risk of malaria, deaths associated with malaria are at increasing rate and the need of a web-based system that could diagnose malaria and provide therapy. A machine learning technique Rough Set was used on training set to generate a classification model for malaria diagnosis for different malaria cases and therapy was provided accordingly.
A Fuzzy Expert System for the management of malaria was developed by Djam et al in [2011]. As a prominent environmental health problem in Africa, malaria constitutes a great threat to the existence of many communities and the complexities in medical practice make traditional quantitative approaches of analysis inappropriate. Fuzzy techniques were incorporated on data collected and fuzzy expert system was developed for the management of malaria.
In Ugwu et al., (2009), The Application of Machine Learning Techniques for malaria diagnosis. Insufficiency of medical specialist, which has increased the mortality of patients who suffer from malaria and the need to use computer technology to reduce the number of mortality and reduce the waiting time to see the specialist on malaria. The research methodology adopted is the Structured System and Design Methodology (SSADM).Feasibility study of the manual method for performing a medical diagnosis was carried out. The potential of decision tree was used for the design of the system to overcome the weaknesses of the manual method.
Kamukama in [2010] developed A Clinical Protocol-Based Decision Support System for Malaria Treatment. The medical field has become overwhelmed by large volume of data to manage, resulting into variations in treatment processes, which sometimes lowers quality of service, malaria has continued to be a global scourge, killing several millions of people annually, the vast majority of deaths occur among young children and pregnant women in Africa, especially in remote rural arrears with poor access to health services, Protocol non-compliance, inadequate knowledge and expertise are all responsible for these millions of death. Research and Review of the existing literature on concepts underlying Protocol Based Decision Support System including the critical elements needed in their development and the technology .Knowledge acquisition for the knowledge base of the Protocol-Based Decision Support System, Analysis and Representation ,Data manipulation and Inference, System Development, Testing and Validation, Documentation.
Fuzzy-rule based framework for the management of tropical diseases, using malaria as a case study was developed by Obot and Uzoka in [2009]. The application of the conventional symbolic rules found in knowledge base technology to the management of a disease suffers from its inability to evaluate the degree of severity of a symptom and by extension, the degree of the illness. The fuzzy logic for the diagnosis of malaria disease involves fuzzification, inference and defuzzification. There were qualitative and quantitative variables, which were fuzzified, inferred and defuziffied. Fuzzification begins with the transformation of raw data. During the process, linguistic labels are attached to the symptoms and the diagnostic steps are accompanied by associated degrees of intensity rated on a likert scale of 1 – 5. The linguistic labels are later assigned some degrees of membership for mild, moderate, severe and very severe labels and fuzzy rules are then developed. The fuzzy inference employed is root sum square (RSS) and the deffuzification inference is a mapping from a space of fuzzy actions defined over an output universe of disclosure into a space of non-fuzzy actions.
Uzoka and Barker [2005] in Medical Decision Support System using Analytical Hierarchy Process: A case study of malaria diagnosis. Malaria attack is so prevalent, especially in the tropics, malaria is a major source of morbidity and mortality in most African countries, high incidence among children less than 5 years old, roll back malaria has not succeeded in eradicating malaria, research has been intensified in the past decade to facilitate finding more appropriate means of malaria diagnosis, treatment and control. The method used involved interaction with medical doctors on symptoms of malaria, the possible grouping of the symptoms and the pairwise comparison of the symptoms. Design of a computer oriented model using the analytical hierarchy process (AHP) powered inference mechanism. The major components of the model are Knowledge base, Decision Support base (Powered by AHP) and User interface.
Decision Support Systems to identify different species of malaria parasites was developed by Prabhu et al in [2003]. The research was motivated due to the fact that timely and accurate diagnosis of different species of malaria is essential to prevent mortality and morbidity. In the method used, two expert systems were developed to aid in the diagnosis of malaria. A rule based decision support tool (CLIPS) was used to create the medical expert system. A limited Bayesian prototype was also developed in Netica, to compare and assess the usefulness of probabilistic systems. Certain assumptions were made to formalise the knowledge in both the rule based and Bayesian systems.
Olabiyi et al in [2016] presented A Decision Support System for Diagnosing Tropical Diseases Using Fuzzy Logic. The motivation for this research include- Tropical diseases are associated with a high level of mortality rate, and also they are very common in tropical countries, the tropical diseases have some similar symptoms which makes it difficult sometimes for doctors to diagnose, patients sometimes have difficulty explaining how they feel to the doctors, some doctors are not familiar with some of the new changes in medicine and human health.
Data were gathered by interacting with various medical doctors who are experts in diagnosing tropical diseases to gain heuristic knowledge on the diseases. The system was developed to diagnose ten tropical diseases including malaria. Diagnosis was carried out by weighing each symptom with respect to the disease in question using generalized fuzzy soft set (GFSS).
A Knowledge-Based Data Mining System for Diagnosing Malaria Related Cases in Healthcare Management was developed by Olugbenga et al [2010]. Research Motivations: In some hospitals in Nigeria, it is difficult to select or extract very important information from the database, the increasing volume of data in modern business and science, most especially the health sector calls for computer based approaches. Data collection was obtained by survey from four hospitals in Lagos metropolis of Nigeria. Visualization and knowledge representation techniques were used to present the mined knowledge to the user. The components of the knowledge based data mining system are: knowledge base, inference engine, rules and decisions. The implementation of the system was carried out using C#.NET programming language and Microsoft SQL Server 2005.
Computer Automation for Malaria Parasite Detection Using Linear Programming by Vipul et al [2013]. Malaria causes more than 1 million deaths arising from approximately 300-500 million infections every year. Manual microscopy is not a reliable screening method when performed by non-experts. Need of an automated system aims at performing this task without human intervention and to provide an objective, reliable and efficient tool to do so. Formulation of a linear programming model based on the given data. Solving and displaying the result using graphical method approach for detecting parasite.
CHAPTER THREE
METHODOLOGY
INTRODUCTION
The chapter contains the methodology and procedure employed in carrying out the study. It will be discussed under the following headings;
- Methodology
- Strength of the present system
- Weakness of the present system
- Analysis of proposed system
- Description of input and output document
- Information and product flow diagram
- Specification and design
- Overview description of the new system
- High level model of the proposed system
Methodology
The research methodology adopted is the Structured System and Design Methodology (SSADM).Feasibility study of the manual method for performing a medical diagnosis was carried out. The potential of decision tree was used for the design of the system to overcome the weaknesses of the manual method.
STRENGTH OF THE PRESENT SYSTEM
The existing system in this case is the manual method of hospitals and health organization to diagnose prevent and cure of malaria.
Our numerous hospitals or health centre’s are normally equipped with waiting room where patients wait and take turns to see the doctor/medical practitioner. In this waiting room, patients usually spent a lot of time waiting to consult the doctor, while they are waiting, they are opportune to pick pamphlet about the diagnosis and treatment of malaria from a box, normally kept in a corner of the waiting room.
The patients read the information in Pamphlets and re able to get some information. For that information that is not clear seeking explanation from the doctor/medical practitioner in some case, they seek for such clarification from the available nurses. When new information is to be added, it means that new pamphlets have to be printed. Printing the pamphlets is taken care of by the federal ministry of health who then sent such pamphlets to the appropriate authorities.
WEAKNESS OF THE PRESENT SYSTEM
The writing of this project was prompted by the problems encounted by the existing systems which are,
- Low speed of data entry
- Lack of accuracy
- The present system is manual and not reliable
- Ignorance on the part of the patients
- The present system lack references
REFERENCES
- Adebor A.B ad Burrell P.R (2008), “An intelligent Decision Support System for the Prompt Diagnosis of Malaria and Typhoid fever in the Malaria Belt of Africa”, Artificial Intelligence in Theory and Practice II, Pg288-295.
- Adehor A.B and Burrell P.R. (2008), “The Integrated Management of Health Care Strategies and Differential Diagnosis by Expert System Technology: A Single- Dimensional Approach”, World Academy of Sciences, Engineering and Technology 20 2008 pp 533-538,
- Adetunmbi A.O, Oguntimilehin A., and Falaki S.O (2012), “Web-Based Medical Assistant System for Malaria Diagnosis and Therapy”, GESJ: Computer Science and Telecommunications No1(33), Pg 42-53.
- Agbonifo O.T. and Akinyede O.R. (2008), “Modelling a framework for telemedicine in transmitting Medical Data for diagnosis
- Bernard C, Jacques T and Henny B (2000), “Script and Medical Diagnostic knowledge: Theory and Applications for Clinical Reasoning Instruction and research”.
- Budhathoki CB and BC RK (2008), “Perception of Malaria and Pattern of Treatment seeking behaviour among Tharu and Pahari Communities of Jhalari”, Nepal Health RO counc 6(13) 84-92
- Djam X.Y, Wajiga G.M., Kimbi Y.H and Blamah N.V (2011) “A Fuzzy Expert System for the management of malaria”, International Journal of Pure and Applied Sciences and Technology, pp.84-108, ISSN 2229-6107.
