For example, if the goal is to capture the half-life relationship of protein degradation, a known non-linear process with exponential decay, the use of a linear model will not result in accurate prediction of protein levels at any time point, no matter how many training samples make up the dataset.
By contrast, variance expresses the sensitivity of the model to small perturbations in the input. A model of high variance will provide substantially different answers output values for small changes in the input, because of overfitting of its parameters to the training dataset at hand [ 61 ].
This prevents generalizations and thus the ability of the model to perform well to other datasets never seen by the model, i. In general, variance increases and bias decreases with increasing model complexity [ 36 , 62 ].
Many ML algorithms are susceptible to overfitting because they have a strong propensity to fit the model to the dataset and minimize the loss function as much as possible. Because the goal of ML is to make the model generalizable from learning the training data, and not to obtain the best model well-fitted for the training data, proper measures should be taken depending on the type of algorithm.
The most popular solutions for overfitting are training with more data of high-quality and the least amount of noise, cross-validation, early stopping, pruning remove features , regularization, and ensembling [ 36 , 37 , 61 , 63 ]. The appropriate combination should be selected depending on the purpose of the study, the characteristics and size of the dataset, and the learner type.
However, in predictive modeling, too many features can impede learning because some may be irrelevant to the target of interest, less important than others or redundant in the context of other features. It is statistically advantageous to estimate fewer parameters. In addition, researchers usually want to know the key informative features obtained with a simple model rather than work with a complex model that uses a large number of features to predict the outcome. In truth, processes that make the refined data amenable to learning, such as data cleaning, preprocessing, feature engineering and selection, are more essential than running a learner.
However, this is a daunting task because it is manually tailored by domain experts in a time-consuming process [ 61 ]. Feature engineering is the process of transforming raw data such that the revised features better represent the problem that is of interest to the predictive model, resulting in improved model performance on new data.
Feature selection is the process of selecting a subset of relevant features while pruning less-relevant features for use in model construction. There are three methods in feature selection algorithms: filter methods, wrapper methods, and embedded methods [ 37 , 64 , 65 ]. Filter methods involve the assignment of a score to each feature using a statistical measure followed by selection of high-ranked features based on the score.
Filtering uses a preprocessing step and includes correlation coefficient scores, the pseudo-R 2 statistic and information gain. An example is the recursive feature elimination algorithm. Embedded methods perform variable selection in the process of training and are usually specific to certain learning machines. The features selected from the methods do not necessarily have a causal relationship with the target label, but simply provide critical information for use in predictive model construction. Limitations of machine learning ML has become ubiquitous and indispensable for solving complex problems in most sciences [ 16 ].
It can present novel findings or reveal previously hidden but important features that have been missed or overlooked in conventional studies using traditional statistics. However, those features might also be irrelevant, nonsensical, counterposed to the framework of current medical knowledge, or even cause confusion. This is because the results returned by ML are based solely on the input data. ML does not call the input data into question or explain why the results were obtained or their underlying mechanism.
In the event of unexpected results, the data should be re-investigated to determine whether human or technical errors have created biases, followed by careful interpretation and validation in the context of the disease. ML models are fairly dependent on the data they are trained on or are called upon to analyze, and no model, regardless of its sophistication, can create a useful analysis from low-quality data [ 61 , 66 ].
As data are a product made in the past and represent existing knowledge, ML models are valid within the same framework of that knowledge and their performance will degrade if they are not regularly updated using new, emerging data. In the case of a supervised classifier, a common problem is that the classes that make up the target label are not represented equally. An imbalanced distribution of class sizes across samples favors learning weighted to the larger class size such that the trained model then preferably assigns a major class label to new instances thereof while ignoring or misclassifying minority samples, which, although they rarely occur, might be very important.
Several methods have been devised to handle the imbalanced class issue [ 67 , 68 ]. Because the optimal algorithm, i. In some ML learners, hyperparameters should be tuned by exhaustively searching through a manually specified subset of the hyperparameter space of a learning algorithm [ 69 ]. Randomness is an inherit characteristic of ML applications [ 70 ], appearing in data collections, observation orders, weight assignments, and resampling, among others.
In the study of rheumatic diseases, ML has been employed only recently, but two of those studies are particularly noteworthy. In the first, Orange et al. These labels were used to design a histologic scoring algorithm in which the histologic scores correlated with clinical parameters such as ESR, C-reactive protein CRP level, and autoantibody titer [ 87 ].here
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The authors selected 14 histologic features from synovial samples RA and six osteoarthritis [OA] patients and the most variably expressed genes in 45 synovial samples from 39 RA and six OA patients. Gene-expression-driven subgrouping was explored by k-means clustering, in which n objects are partitioned into k clusters, with each object belonging to the cluster with the nearest mean [ 88 ]. Clustering was most robust at 3 and this subgrouping was validated by principal component analysis, but not in an independent dataset. Three subgroups comprising high-inflammatory, low-inflammatory, and mixed subtypes, were designated based on their gene patterns and enriched ontology.
The aim of the study was to determine the synchrony between synovial histologic features and genomic subtype, thereby yielding a convenient histology-based approach to characterization of synovial tissue.
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To this end, a leave-one-out cross-validation SVM classifier was implemented. The aim of an SVM is to find a decision hyperplane that separates data points of different classes with a maximal margin i. It should be noted that histologic subtypes are closely associated with clinical features, as significant increases in ESR, CRP levels, rheumatoid factor titer, and anti-cyclic citrullinated protein CCP titer in patients with high inflammatory scores were detected.
However, this model might succumb to overfitting because SVM is vulnerable to overfitting [ 89 , 90 ], the sample size was too small only 45 samples and the model was not validated using an independent dataset. Moreover, the data samples were a mixture of RA and OA samples and there were no normal controls.
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SVM is an unsupervised ML with an efficient performance achieved using the kernel trick and the tuning of hyperparameters. A better approach would be to specify the details of the model kernel type, parameters, and hyperparameters during method selection, to guarantee the reliability and reproducibility of the model. In the second, Lezcano-Valverde et al.
RSF, an extension of random forest for time-to-event data, is a non-parametric method that generates multiple decision trees using a bagging method [ 92 , 93 ]. Bagging, an abbreviation for bootstrap aggregation, is a simple and powerful ensemble method that fits multiple predictive models on random subsets of the original dataset and aggregates their individual predictions by either voting or averaging [ 94 ].
It is commonly used to reduce variance and avoid overfitting. RSF is an attractive alternative to the Cox proportional hazards model when the proportional hazards assumption is violated [ 93 , 95 ].
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Lezcano-Valverde et al. Each model was run times using 1, trees per run. The prediction error was 0. Important variables with a higher predictive capacity were age at diagnosis, median ESR and number of hospital admissions. These variables were consistent with those obtained in a previous result using a Cox proportional hazards model [ 96 ]. The strengths of the approach described in that study were external validation using an independent RA cohort and the absence of a restrictive assumption, which traditional Cox proportional hazards model rely on. RSF has also been used to analyze the mortality risk in patients with systemic lupus erythematosus [ 97 ] and in those with juvenile idiopathic inflammatory myopathies [ 98 ].
Extensive, in-depth applications of ML in biomedical science are increasing in number, and interesting results in the area of precision medicine have been obtained. However, several challenges must still be overcome. First, ML works only if the training data are representative of the problem to be solved, include informative features and are of sufficient quantity to train the model at hand.
This can be difficult to achieve for both technical and real-world reasons. Second, privacy is a major concern in the collection of sensitive clinical data, which might limit the aggregation of all necessary information. Moreover, some data are expensive to acquire, reported in different formats and obtained using different methods and technologies. Third, because text-based medical records can be incoherent, distracted, and contain technical errors [ 52 , 53 ], expert human judgement is needed to review the data, detect any errors or problems and determine the clinical significance of any findings [ 35 , 99 ].