Abstract
Liver ultrasound scan (US) use in diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD) causes costs and waiting lists overloads. We aimed to compare various Machine learning algorithms with a Meta learner approach to find the best of these as a predictor of NAFLD. The study included 2970 subjects, 2920 constituting the training set and 50, randomly selected, used in the test phase, performing cross-validation. The best predictors were combined to create three models: 1) FLI plus GLUCOSE plus SEX plus AGE, 2) AVI plus GLUCOSE plus GGT plus SEX plus AGE, 3) BRI plus GLUCOSE plus GGT plus SEX plus AGE. Eight machine learning algorithms were trained with the predictors of each of the three models created. For these algorithms, the percent accuracy, variance and percent weight were compared. The SVM algorithm performed better with all models. Model 1 had 68% accuracy, with 1% variance and an algorithm weight of 27.35; Model 2 had 68% accuracy, with 1% variance and an algorithm weight of 33.62 and Model 3 had 77% accuracy, with 1% variance and an algorithm weight of 34.70. Model 2 was the most performing, composed of AVI plus GLUCOSE plus GGT plus SEX plus AGE, despite a lower percentage of accuracy. A Machine Learning approach can support NAFLD diagnosis and reduce health costs. The SVM algorithm is easy to apply and the necessary parameters are easily retrieved in databases.
Highlights
Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease in Western countries, as well as a condition raising the risk for cardiovascular diseases, type 2 diabetes mellitus and chronic renal disease, and increased mortality [1, 2]
Non-Alcoholic Fatty Liver Disease (NAFLD) can manifest as pure fatty liver disease or as non-alcoholic steatohepatitis (NASH), an evolution of the former where the steatosis is associated with inflammation and hepatocellular injury, and with fibrogenic activation that can lead to cirrhosis and the onset of hepatocarcinoma [6]
Machine Learning has already been used as a support tool for Machine learning in NAFLD diagnosis the diagnosis of some diseases and for risk quantification, such as Cardiovascular risk in patients with Diabetes Mellitus [11, 12], Ischemic Heart Disease [13] and Cancers [14]
Summary
Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease in Western countries, as well as a condition raising the risk for cardiovascular diseases, type 2 diabetes mellitus and chronic renal disease, and increased mortality [1, 2].Worldwide, NAFLD prevalence is currently estimated to be around 24% and is constantly increasing (from 15% in 2005 to 25% in 2010).A meta-analysis published in 2016 reported an average prevalence of 23.71% in Europe [3]. Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease in Western countries, as well as a condition raising the risk for cardiovascular diseases, type 2 diabetes mellitus and chronic renal disease, and increased mortality [1, 2]. NAFLD prevalence is currently estimated to be around 24% and is constantly increasing (from 15% in 2005 to 25% in 2010). NAFLD can manifest as pure fatty liver disease (hepato-steatosis) or as non-alcoholic steatohepatitis (NASH), an evolution of the former where the steatosis is associated with inflammation and hepatocellular injury, and with fibrogenic activation that can lead to cirrhosis and the onset of hepatocarcinoma [6]. Liver ultrasound scan (US) use in diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD) causes costs and waiting lists overloads.
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