Mesopotamian Journal of Computer Science

An Extensive Examination of the IoT and Blockchain Technologies in Relation to their Applications in the Healthcare Industry

2024-01-14T07:14:15+00:00

Numerous domains have been transformed by the communication technologies made possible by the Internet of Things (IoT), one of which is health monitoring systems. Patterns associated with diseases and health conditions can be identified through the utilization of machine learning and cutting-edge AI techniques. Currently, scientific endeavours are concentrated on enhancing IoT-enabled applications such as medical report administration, prescription traceability, and infectious disease surveillance through the amalgamation of blockchain technology(BCT) and machine learning(ML) models. Although recent advancements have attempted to increase the adaptability of blockchain(BC) and ML for IoT applications, there are still a number of crucial considerations that must be made for improved outcomes. This report provides a comprehensive examination of emerging technologies in the healthcare sector, encompassing the IoT, and blockchain.

New Recruitment Approach Based on Logistic Regression Model

2023-10-11T07:41:28+00:00

Artificial intelligence (AI) is a pivotal technological advancement developed by humans with the aim of enhancing the quality of human existence. It signifies the capacity of a computerized machine resembling a robot to execute tasks typically performed by humans and replicate human behavior. Machine learning (ML), a subfield of AI, involves the construction of systems that acquire the ability to make predictions about new output values by leveraging existing data, without the need for human interaction. Currently, ML has been incorporated into various fields, including but not limited to medical diagnosis, image processing, prediction, classification, learning association, commerce, finance, and natural language processing. This project aims to employ ML techniques within the human resource (HR) department. The implemented model will enable the human resources department to effectively identify the most appropriate candidates for a job opening throughout the recruitment process, utilizing a comprehensive dataset and considering many criteria, all without the need for manual intervention. The construction of the model involves the utilization of an authentic dataset comprising recruitment tasks. Initially, the dataset undergoes a process of selecting the most pertinent elements from both pre-existing and extracted factors. These selected factors include educational level, age, and past experience. Furthermore, taking into consideration these aforementioned factors, a decision system is constructed utilizing the Binary classification approach. The logistic regression classifier is utilized in this investigation. Subsequently, the dataset is partitioned into two distinct subsets, namely the training subset and the testing subset. The effectiveness of the model is demonstrated by the utilization of various evaluation metrics, including the confusion matrix, recall, precision, accuracy, and F-measure values.

Examining Ghana's National Health Insurance Act, 2003 (Act 650) to Improve Accessibility of Artificial Intelligence Therapies and Address Compensation Issues in Cases of Medical Negligence

2024-02-28T15:26:11+00:00

Objective: Examine Ghana’s National Health Insurance Act (Act 650) to identify coverage gaps limiting artificial intelligence (AI) therapy access and address medical negligence liability issues surrounding automated healthcare systems.

Methods: Legal and regulatory analysis of Act 650 were conducted, review of academic literature on global uptake of AI interventions and medical negligence principles were elucidated, examination of case studies implementing pilot AI therapy programs under insurance schemes were considered.

Results & Conclusions: Act 650 lacks clear provisions for funding innovative AI treatments with proven efficacy and undefined negligence determination guidelines involving AI systems, contributing to accessibility and accountability issues. Proposed amendments to reimburse certain AI therapies through the National Health Insurance Scheme, expand certified provider eligibility, and institute transparent negligence compensation formulas.

Recommendations: Reform Act 650 to support increased appropriate use of AI healthcare services, protect patients undergoing automated diagnosis/treatment, and clarify liability rules for medical negligence incidents relating to AI.

Novelty & Significance: First extensive analysis focused on opportunities for Ghana’s health insurance framework to catalyze equitable diffusion of advanced AI therapeutics and address emerging legal challenges and safety risks as automated medicine advances.

A Comparative study of Chest Radiographs and Detection of The Covid 19 Virus Using Machine Learning Algorithm

2024-03-06T21:21:13+00:00

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak that is causing coronavirus disease 2019 is being deemed a pandemic because of its quick spread around the globe. Because chest X-ray pictures have shown to be beneficial in monitoring a variety of lung disorders, they have recently been utilized to monitor COVID-19 disease. It takes time to manually analyze a lot of chest X-ray pictures. Several previous studies have suggested machine-learning (ML)-based techniques for COVID-19 detection from chest X-ray pictures as a solution to this issue. Though little effort has been made to use traditional machine learning (ML) methods, the majority of these investigations use deep learning (DL) based techniques. Conventional ML-based algorithms will be favored for implementation if they can yield identical outcomes as DL-based methods. In this effort, we constructed four classic ML-based models for COVID-19 identification, driven by the need to close the gap in the literature. The accuracy rates for the various classification models were as follows, according to the results: 93.4% for Support Vector Machine (SVM), 93.3% for Random Forest (RF), 90.5% for K-Nearest Neighbors (KNN), and 87.9% for Decision Tree (DT). The results of the study showed that machine learning-based algorithms can produce great results for COVID-19 identification by being refined and improved using several well-known data preparation approaches.