The adoption of information technology in healthcare has resulted in novel solutions for improving patient care and operational efficiency. This study details the design and implementation of a web-based clinic management system augmented with sentiment analysis to evaluate and enhance patient satisfaction. The system utilizes natural language processing and machine learning to autonomously assess patient feedback, understanding feelings (negative, positive, and neutral) regarding critical aspects such as waiting times, doctor-patient interactions, care efficacy, and overall clinic experience. The system underwent alpha and beta testing, commencing with controlled trials and then involving real-world evaluations with clinic attendants, physicians, and patients. An evaluation conducted in a dermatology clinic revealed the system's effectiveness in detecting service deficiencies and informing enhancements. Thus, this study suggests that the integration of sentiment analysis in clinical management systems enhances data-driven decision-making, hence improving patient experiences and optimizing operations.

Data analysis is an integral part of research. Most researchers examine their results by using graphs, tables, charts, and figures. These methods are effective, but knowledge transfer is limited because it only depends on what the authors or researchers have presented. The need to scrutinise further the given data is essential. One way of addressing this problem is to utilise a graphical user interface (GUI), wherein a user can manually choose some parameters of an extensive dataset to display and analyse. In this paper, the results of the four variants of clustering techniques, namely the Ant Colony Optimization (ACO), Gaussian Mixture Model (GMM), K-Power Means (KPM), and Kernel-Power Density-Based Estimation (KPD), in grouping the wireless multipath propagations, are evaluated through the use of a GUI. The accuracy performance of each clustering algorithm can be obtained by choosing in the GUI the corresponding channel scenario that the user would like to investigate. A deeper analysis of the clustering characteristics can also be done by selecting other parameters in the GUI. This selection gives a better understanding of the behaviour of each clustering technique and provides an effective way of presenting and analysing the different sets of data.