伤寒和疟疾扩展医疗诊断系统的开发

IF 1.7 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Temitope Apanisile, Joshua Ayobami Ayeni
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引用次数: 0

摘要

在尼日利亚等发展中国家,疟疾和伤寒是当今社会面临的主要健康挑战。症状各不相同,可导致身体出现其他疾病,包括长期发烧、疲劳、恶心、头痛,如果诊断和治疗不当,还可能同时发生感染。发展中国家迫切需要具有成本效益的技术来管理疾病进程并降低发病率和死亡率。卫生保健面临的一些具有挑战性的问题是缺乏适当的数据处理和传播卫生信息的延迟,这往往导致提供结果的延迟和提供服务的质量差。本文通过开发人工智能(AI)驱动的扩展诊断系统(EDS)来解决现有系统的弱点。该数据集来自拉各斯大学教学医院(LUTH)的患者历史记录,包含250条记录,有5个属性,如风险水平、性别、症状1、症状2和疾病类型。对疟疾和伤寒数据集进行了预处理和清理,以删除不需要的数据和信息。EDS使用朴素贝叶斯技术开发,并使用软件开发工具实现。使用以下已知指标评估系统的性能:真阳性(TP)、真阴性(TN)、假阳性(FP)和假阴性(FN)的准确性。EDS对疟疾和伤寒的疗效显著。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of an Extended Medical Diagnostic System for Typhoid and Malaria Fever
In developing countries like Nigeria, malaria and typhoid fever are major health challenges in society today. The symptoms vary and can lead to other illnesses in the body which include prolonged fever, fatigue, nausea, headaches, and the risk of contracting infection occurring concurrently if not properly diagnosed and treated. There is a strong need for cost-effective technologies to manage disease processes and reduce morbidity and mortality in developing countries. Some of the challenging issues confronting healthcare are lack of proper processing of data and delay in the dissemination of health information, which often causes delays in the provision of results and poor quality of service delivery. This paper addressed the weaknesses of the existing system through the development of an Artificial Intelligence (AI) driven extended diagnostic system (EDS). The dataset was obtained from patients’ historical records from the Lagos University Teaching Hospital (LUTH) and contained two-hundred and fifty (250) records with five (5) attributes such as risk level, gender, symptom 1, symptom 2, and ailment type. The malaria and typhoid dataset was pre-processed and cleansed to remove unwanted data and information. The EDS was developed using the Naive Bayes technique and implemented using software development tools. The performance of the system was evaluated using the following known metrics: accuracies of true positive (TP), true negative (TN), false positive (FP), and false negative (FN). The performance of the EDS was substantially significant for both malaria and typhoid fevers.
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
22
审稿时长
4 weeks
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