Hepatic diseases taking the lives of as many as 1.75 million people worldwide annually are a menace to be reckoned with. (1) Younossi ZM et al (1) indicated that nonalcoholic fatty liver disease (NAFLD) is the only hepatic pathology growing exponentially in its prevalence, in coincidence with the increasing rates of both; obesity as well as type 2 diabetes mellitus (T2DM): all wreaking havoc. (2) (3)(4) Where COVID-19 pandemic is undeniably encroaching over the global health system's resources, NAFLD, along with other ailments, is bound to be ignored which may lead to a rise in the associated morbidity & mortality.
A considerable amount of financial resources are being put in globally in the fight against COVID-19 pandemic where radiological diagnosis for NAFLD demands a considerable allocation of funds, the prioritization of resources shall undeniably end up in missing cases on the cost of the patients as well as their families in terms of the psychological burden. A low-cost, accurate system to screen patients for a potential NAFLD diagnosis using simple, easy-to-measure anthropometric measures is a dire need of time, thus.
Huang BX et al, in a cross-sectional study conducted in the Health Examination Center in Guangzhou, China took in anthropometric measures & abdominal ultrasonography & concluded neck circumference to be an independent predictor for the fatty liver disease. (5) The purpose of this study is to create such machine learning models that take in the said easy-to-measure variables from the Huang BX et al study (5) & come up with an autoML protocol for initial screening diagnosis for NAFLD: models that are, if not having a potential to replace, should be at least comparable with the abdominal ultrasound screening diagnosis for NAFLD. By adopting Mljar (6) , a zero-code autoML web platform providing feature preprocessing and eningeering, algorithm training and hyperparameters selection bundle for machine learning, we are able to create such practical models.
The study takes in data (7) from Huang BX et al. (5) . The authors took in 4053 subjects, 2436 men and 1617 women between 20 and 88 years of age, after excluding those patients that had a history of co-morbid conditions as well as those with a lack of heaptic ultrasonography data. Patients' history records were inquired & state-of-theart methods were adopted to measure anthropometric & biochemical variables leaving negligible measurement errors, only. Contrary to South Asian standards where BMI of ≥ 25 Kg/m 2 is termed as Overweight, a BMI ≧24 kg/m 2 , for both genders, is termed as Overweight by Huang BX et al. (5) , citing Zhou B (8) . The Graif's criteria (9) was adopted to diagnose Fatty liver disease on ultrasonography.
Homogenous Development Framework MLjar (6) , an AutoML zero-code machine learning web platform, is a complete package for data loading, pre-processing, modelling & result interpretation with a considerably high quality of machine learning models which can be deployed both locally & across a rest API. A homogenous approach (Table 1 ) was adopted for the development of the models vis-à-vis the preprocessing & tuning protocols as well as system specifications so as to keep the model development bias to a minimum. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint 
Henceforth mentioned machine learning models were, thus, created:
Since the class imbalance was considerably high, the discriminative ability of the models were the primary outcome variables. AUC-ROC analysis was adopted to measure that ability. (10) The respective values were interpreted in accordance with the schema provided by Lau L et al. (10) (Table 2) AUC-ROC value Interpretation >0.9
Excellent discrimination >0.75
Good discrimination >0. 5 Random guessing is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted October 22, 2020. ; https://doi.org/10.1101/2020.10.20.20216291 doi: medRxiv preprint Secondarily, training time was also analyzed. Ideally, the best model shall be the one that has the highest discriminating capacity & yields results within the smallest time period.
The study adopted a zero-code ML platform to come up with 8 types of machine learning models that take in easy-to-measure anthropometric measures such as BMI & waist-to-hip ratio in order to provide a screening diagnosis for NAFLD. As indicated in table 3, all of the algorithms, trained in accordance with the aforementioned Homogenous Development Framework, have good discriminating ability to designate the dichotomous variable of interest.
RF came out to have the highest discriminating ability, with a computation time of 4 minutes 9 seconds. Out of the proposed models, KNN had the least AUC but a considerably less computation time of only 6 seconds. LGBM required as much as 10 minutes to come up with a considerable AUC. LR completed its computation in the least amount of time.
Among the ensemble averages, RGF achieved the highest average. Given that the best model of RGF was up with its training in only 10 seconds & an additional 35 seconds were lapsed for ensemble averaging, RGF outperforms all others models by achieving the highest AUC & thus exhibiting the best discriminating ability out of all the models. KNN is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted October 22, 2020. ; https://doi.org/10.1101/2020.10.20.20216291 doi: medRxiv preprint Table 3 . AUC & training time of the proposed models
Many studies have been done to utilize machine learning for the prediction of fatty liver disease. Atabaki-Pasdar N et al (11) in a major modelling & validation study concluded that the highest AUC (of 0.84 for the respective study) is obtained by the combination of "-omics" data & clinical variables. Using MRI-derived proton density fat fraction for referencing, Han A et al (12) developed deep learning one-dimensional convolutional neural networks for NAFLD diagnosis by taking in ultrasound data. 1 By taking in all the patients who had been screened for fatty liver at the New Taipei City Hospital between the 1st and 31 st of December 2009, Wu CC et al (13) developed several classification models to predict fatty liver disease and obtained the highest AUC of 0.925 on a Random Forest model. Feature selection was employed to obtain the best variables to be fed in the models, here. The utilization of machine learning to predict hepatic pathologies in general & NAFLD in particular is thus evident. Our proposed models are the very first effort, to the best of our knowledge, to leverage autoML zero-code platforms to come up with machine learning models that are trained to have a good discriminating ability to predict NAFLD using only anthropometric measures. The proposed models neither require costly analysis so that variables, such as unltrasonographic signals, may be fed in them to obtain a prediction nor does it require considerably high computation time & resources.
This been stated, the model does require external validation using data from populations different from its training population. Only thus can a machine learning model's generalization can be truly validated. Moreover, a study comparing the presented model's diagnosis with an abdominal ultrasound diagnosis for NAFLD, the predictions assessed against hepatic biopsy, is proposed to be in order to explore the presented models' potential to replace abdominal ultrasound as an initial diagnostic tool for NAFLD.
Since autoML platform was adopted, the proposed models are analogous to a black-box, the internal workings of which are difficult to decipher. Moreover, the computation time of the best LR model is only 1 second which might possibly be due to overfitting of the respective model.
The presented models indicate that the fusion of machine learning & medicine is fruitful for cutting down the associated costs of screening and initial diagnosis of NAFLD: an ailment that has considerable morbidity and mortality associated with it. 1 For the Han A et al (12) study, the metrics against which the respective proposed model was evaluated did not include AUC.
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is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted October 22, 2020. ; https://doi.org/10.1101/2020.10.20.20216291 doi: medRxiv preprint
By adopting an autoML zero-code platform, machine learning models with good discriminating ability are presented that require only easy-to-measure anthropometric measures as input variables to come up with an initial screening diagnosis for NAFLD. Further studies should be conducted to compare the proposed models with abdominal ultrasound for the screening diagnosis of NAFLD.
The authors report no potential conflict of interest whatsoever.
This study was not funded by any institution. We extend our token of appreciation towards mljar (https://mljar.com/).