Rankovic Nevena, Rankovic Dragica, Lukic Igor, Savic Nikola, Jovanovic Verica
Department of Cognitive Science and Artificial Intelligence, School of Humanities and Digital Sciences, Tilburg University, 5037 AB Tilburg, The Netherlands.
Department of Mathematics, Informatics and Statistics, Faculty of Applied Sciences, Union University "Nikola Tesla", 18 000 Nis, Serbia.
J Pers Med. 2023 Jun 22;13(7):1032. doi: 10.3390/jpm13071032.
In previous years, significant attempts have been made to enhance computer-aided diagnosis and prediction applications. This paper presents the results obtained using different machine learning (ML) algorithms and a special type of a neural network map to uncover previously unknown comorbidities associated with chronic diseases, allowing for fast, accurate, and precise predictions. Furthermore, we are presenting a comparative study on different artificial intelligence (AI) tools like the Kohonen self-organizing map (SOM) neural network, random forest, and decision tree for predicting 17 different chronic non-communicable diseases such as asthma, chronic lung diseases, myocardial infarction, coronary heart disease, hypertension, stroke, arthrosis, lower back diseases, cervical spine diseases, diabetes mellitus, allergies, liver cirrhosis, urinary tract diseases, kidney diseases, depression, high cholesterol, and cancer. The research was developed as an observational cross-sectional study through the support of the European Union project, with the data collected from the largest Institute of Public Health "Dr. Milan Jovanovic Batut" in Serbia. The study found that hypertension is the most prevalent disease in Sumadija and western Serbia region, affecting 9.8% of the population, and it is particularly prominent in the age group of 65 to 74 years, with a prevalence rate of 33.2%. The use of Random Forest algorithms can also aid in identifying comorbidities associated with hypertension, with the highest number of comorbidities established as 11. These findings highlight the potential for ML algorithms to provide accurate and personalized diagnoses, identify risk factors and interventions, and ultimately improve patient outcomes while reducing healthcare costs. Moreover, they will be utilized to develop targeted public health interventions and policies for future healthcare frameworks to reduce the burden of chronic diseases in Serbia.
在过去几年中，人们为增强计算机辅助诊断和预测应用做出了重大努力。本文展示了使用不同机器学习（ML）算法和一种特殊类型的神经网络图所获得的结果，以揭示与慢性疾病相关的先前未知的合并症，从而实现快速、准确和精确的预测。此外，我们正在对不同的人工智能（AI）工具进行比较研究，如科霍宁自组织映射（SOM）神经网络、随机森林和决策树，用于预测17种不同的慢性非传染性疾病，如哮喘、慢性肺病、心肌梗死、冠心病、高血压、中风、关节炎、下背部疾病、颈椎病、糖尿病、过敏、肝硬化、泌尿系统疾病、肾脏疾病、抑郁症、高胆固醇和癌症。该研究是在欧盟项目的支持下作为一项观察性横断面研究开展的，数据收集自塞尔维亚最大的公共卫生研究所“米兰·约万诺维奇·巴图特博士”。研究发现，高血压是舒马迪亚和塞尔维亚西部地区最普遍的疾病，影响9.8%的人口，在65至74岁年龄组中尤为突出，患病率为33.2%。使用随机森林算法还可以帮助识别与高血压相关的合并症，确定的合并症数量最多为11种。这些发现凸显了ML算法在提供准确和个性化诊断、识别风险因素和干预措施以及最终改善患者预后同时降低医疗成本方面的潜力。此外，它们将被用于为未来的医疗框架制定有针对性的公共卫生干预措施和政策，以减轻塞尔维亚的慢性病负担。
