Enoch O. Nwachukwu

Neural Networks and Decision Trees For Eye Diseases Diagnosis

Clinical Decision Support Systems (CDSS) provide clinicians, staff, patients, and other indi‐ viduals with knowledge and person-specific information, intelligently filtered and present‐ ed at appropriate times, to enhance health and health care [1]. Medical errors have already become the universal matter of international society. In 1999, IOM (American Institute of Medicine) published a report “To err is Human” [2], that indicated: First, the quantity of medical errors is incredible, the medical errors had already became the fifth lethal; Second, the most of medical errors occurred by the human factor which could be avoid via the com‐ puter system. Improving the quality of healthcare, reducing medical errors, and guaranty‐ ing the safety of patients are the most serious duty of the hospital. The clinical guideline can enhance the security and quality of clinical diagnosis and treatment, its importance already obtained widespread approval [3]. In 1990, clinical practice guidelines were defined as “sys‐ tematically developed statements to assist practitioner and patient decisions about appropri‐ ate health care for specific clinical circumstances” [4].

A Hybrid Prediction System for American NFL Results

This research work investigates the use of machine learning algorithms (Linear Regression and K-Nearest Neighbour) for NFL games result prediction. Data mining techniques were employed on carefully created features with datasets from NFL games statistics using RapidMiner and Java programming language in the backend. High attribute weights of features were obtained from the Linear Regression Model (LR) which provides a basis for the K-Nearest Neighbour Model (KNN). The result is a hybridized model which shows that using relevant features will provide good prediction accuracy. Unique features used are: Bookmakers betting spread and players’ performance metrics. The prediction accuracy of 80.65% obtained shows that the experiment is substantially better than many existing systems with accuracies of 59.4%, 60.7%, 65.05% and 67.08%. This can therefore be a reference point for future research in this area especially on employing machine learning in predictions.