The Application of Machine Learning Technique for Malaria Diagnosis

Abstract

Healthcare delivery in African nations has long been a worldwide issue, which is why the United Nations and World Health Organization seek for ways to alleviate this problem and thereby reduce the number of lives that are lost every year due to poor health facilities and inadequate health care administration. Healthcare delivery concerns are most predominant in Nigeria and it became imperatively clear that the system of medical diagnosis must be automated. This paper explores the potential of machine learning technique (decision tree) in development of a malaria diagnostic system. The decision tree algorithm was used in the development of the knowledge base. Microsoft Access and Java programming language were used for database and user interfaces, respectively. During the diagnosis, symptoms are provided by the patient in the diagnostic system and a match is found in the knowledge base.

Prediction of leptospirosis cases using classification algorithms

“…The author emphasised the importance of having computational tools that could aid in disease diagnosis, but warned of the need to evaluate the accuracy and reliability of these tools before putting them into clinical use [13]. Of particular relevance to the work presented here, Ugwu et al [14] used decision tree as part of the diagnosis and treatment recommendation for malaria. This is a specific example of a well‐designed algorithm serving as an important health tool in a region with a shortage of specialised professionals.…”

Improved Methods for Estimating Areas under the Receiver Operating Characteristic Curves

“…In the conventional Bi-normal ROC model the estimation of sensitivity and specificity parameters plays an important role since the optimal cut point can be easily derived from these parameters. Ugwu et. Al (2010) explored the potency of machine learning technique (decision tree) in development of a malaria diagnostic system.…”

Neural Predictive Controller Based Diesel Injection Management System for Emission Minimisation

“…The use of ANN for handling large scale databases is established by Ayodele and Absalom (2010). The use of machine learning systems for social projects by Ugwu, Onyejegbu, and Obagbuwa (2010) has established that the machine learning model can be applied to diverse fields of knowledge.…”