Applications of Machine Learning in Cancer Prediction and Prognosis

By Joseph A. Cruz, David S. Wishart. Cancer Informatics, 2006

1,272 citations recorded by the source corpus.

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Subfields: Artificial Intelligence, Health Information Management, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8a0f17e0ee66ad5f50cd35932747e6a806ef03cf

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Citations in the corpus

Machine learning applications in cancer prognosis and prediction (2014, background): “The prediction of cancer outcome usually refers to the cases of (i) life expectancy, (ii) survivability, (iii) progression and (iv) treatment sensitivity [3].”
Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors and Machine-learning Approaches (2017, background): “In supervised learning, the goal is to learn from a certain portion of trained samples with known class labels and to predict classes or numeric values for unknown patterns from large and noisy datasets.(47) Conversely, unsupervised learning finds the natural structure from data without having any p”
Machine Learning for Survival Analysis: A Survey (2017, background): “Recently, the authors in [Cruz and Wishart 2006] and [Kourou et al. 2015] discussed the applications in cancer prediction and provided a comparison of several machine learning techniques.”
Preventing undesirable behavior of intelligent machines (2019, background): “Rather, the papers by Grabczewski and Duch [20] and subsequent machine learning researchers provided tools that researchers with the appropriate domain knowledge and medical expertise could apply properly to cancer treatment [22, 150, 151, 152].”
Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models (2019, background): “…(ML) [29, 37, 58, 65], people are beginning to use ML to address important societal problems like identifying and predicting cancerous cells [14, 32], predicting poverty from satellite imagery to inform policy decisions [27], and locating buildings that are susceptible to catching on fire…”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, methodology): “The potential of using ML-based methods for prediction and prognosis of cancer is highlighted in [70].”
eDoctor: machine learning and the future of medicine (2018, methodology): “Within Oncology, a trend towards using artiﬁcial neural networks has developed and multiple studies are demonstrating superior predictive accuracy using ML techniques in comparison with expert-based or statistical systems [34].”
Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison (2021, background): “DT is recognized as easy to understand and learn and is a basic component of many protocols for medical diagnosis [35].”
Artificial Intelligence in Surgery: Promises and Perils (2018, background): “than conventional statistical analysis.(11,12) ML has outperformed logistic regression for prediction of surgical site infections (SSIs) by building nonlinear models that incorporate multiple data sources, including diagnoses, treatments, and laboratory values.”
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “symptoms classification and analysis to improve diagnostic accuracy, identifying imaging biomarkers of neurological and psychiatric disease, validation for multiple myeloma and breast cancer by analyzing SNPs, hold-out for breast and cervical cancer by analyzing clinical, pathologic and epidemiologi”