Hatami Behzad, Asadi Farkhondeh, Bayani Azadeh, Zali Mohammad Reza, Kavousi Kaveh
Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Clin Chem Lab Med. 2022 May 24;60(12):1946-1954. doi: 10.1515/cclm-2022-0454. Print 2022 Nov 25.
The aim of the study was to implement a non-invasive model to predict ascites grades among patients with cirrhosis.
In the present study, we used modern machine learning (ML) methods to develop a scoring system solely based on routine laboratory and clinical data to help physicians accurately diagnose and predict different degrees of ascites. We used ANACONDA3-5.2.0 64 bit, free and open-source platform distribution of Python programming language with numerous modules, packages, and rich libraries that provide various methods for classification problems. Through the 10-fold cross-validation, we employed three common learning models on our dataset, k-nearest neighbors (KNN), support vector machine (SVM), and neural network classification algorithms.
According to the data received from the research institute, three types of data analysis have been performed. The algorithms used to predict ascites were KNN, cross-validation (CV), and multilayer perceptron neural networks (MLPNN), which achieved an average accuracy of 94, 91, and 90%, respectively. Also, in the average accuracy of the algorithms, KNN had the highest accuracy of 94%.
We applied well-known ML approaches to predict ascites. The findings showed a strong performance compared to the classical statistical approaches. This ML-based approach can help to avoid unnecessary risks and costs for patients with acute stages of the disease.
本研究的目的是实施一种非侵入性模型来预测肝硬化患者的腹水等级。
在本研究中，我们使用现代机器学习（ML）方法，仅基于常规实验室和临床数据开发一个评分系统，以帮助医生准确诊断和预测不同程度的腹水。我们使用ANACONDA3 - 5.2.0 64位，这是Python编程语言的免费开源平台发行版，带有众多模块、包和丰富的库，可为分类问题提供各种方法。通过10折交叉验证，我们在数据集上采用了三种常见的学习模型，即k近邻（KNN）、支持向量机（SVM）和神经网络分类算法。
根据从研究所获得的数据，进行了三种类型的数据分析。用于预测腹水的算法有KNN、交叉验证（CV）和多层感知器神经网络（MLPNN），其平均准确率分别为94%、91%和90%。此外，在算法的平均准确率方面，KNN的准确率最高，为94%。
我们应用了著名的ML方法来预测腹水。研究结果表明，与经典统计方法相比，该方法表现出色。这种基于ML的方法有助于避免疾病急性期患者面临不必要的风险和成本。
