A Supervised Learning Approach by Machine Learning Algorithms to Predict Diabetes Mellitus (DM) Risk Score

Machine learning algorithms are used in language processing, automated driving, and for prediction. Though the theory of machine learning has existed since the 1950s, it was not until the advent of advanced computing that their potential has begun to be realized. Gifted education is a field where machine learning has yet to be utilized, even though one of the underlying problems of gifted education is classification, which is an area where learning algorithms have become exceptionally accurate. We provide a brief overview of machine learning with a focus on neural networks and supervised learning, followed by a demonstration using simulated data and neural networks for classification issues with a practical explanation of the mechanics of the neural network and associated R code. Implications for gifted education are then discussed. Finally, the limitations of supervised learning are discussed. Code used in this article can be found at https://osf.io/4pa3b/

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Deep learning for predicting disease status using genomic data

10.7287/peerj.preprints.27123 ◽

2018 ◽

Cited By ~ 1

Author(s):

Qianfan Wu ◽

Adel Boueiz ◽

Alican Bozkurt ◽

Arya Masoomi ◽

Allan Wang ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Rapid Development ◽

Learning Algorithms ◽

Genomic Data ◽

Disease Status ◽

Machine Learning Algorithms ◽

High Dimensional ◽

Learning Approach ◽

Low Dimensional

Predicting disease status for a complex human disease using genomic data is an important, yet challenging, step in personalized medicine. Among many challenges, the so-called curse of dimensionality problem results in unsatisfied performances of many state-of-art machine learning algorithms. A major recent advance in machine learning is the rapid development of deep learning algorithms that can efficiently extract meaningful features from high-dimensional and complex datasets through a stacked and hierarchical learning process. Deep learning has shown breakthrough performance in several areas including image recognition, natural language processing, and speech recognition. However, the performance of deep learning in predicting disease status using genomic datasets is still not well studied. In this article, we performed a review on the four relevant articles that we found through our thorough literature review. All four articles used auto-encoders to project high-dimensional genomic data to a low dimensional space and then applied the state-of-the-art machine learning algorithms to predict disease status based on the low-dimensional representations. This deep learning approach outperformed existing prediction approaches, such as prediction based on probe-wise screening and prediction based on principal component analysis. The limitations of the current deep learning approach and possible improvements were also discussed.

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Predicting postoperative surgical site infection with administrative data: a random forests algorithm

BMC Medical Research Methodology ◽

10.1186/s12874-021-01369-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yelena Petrosyan ◽

Kednapa Thavorn ◽

Glenys Smith ◽

Malcolm Maclure ◽

Roanne Preston ◽

...

Keyword(s):

Machine Learning ◽

Administrative Data ◽

Risk Score ◽

Random Forests ◽

Learning Algorithms ◽

External Validation ◽

Machine Learning Algorithms ◽

Improvement Program ◽

Health Administrative Data ◽

Administrative Datasets

Abstract Background Since primary data collection can be time-consuming and expensive, surgical site infections (SSIs) could ideally be monitored using routinely collected administrative data. We derived and internally validated efficient algorithms to identify SSIs within 30 days after surgery with health administrative data, using Machine Learning algorithms. Methods All patients enrolled in the National Surgical Quality Improvement Program from the Ottawa Hospital were linked to administrative datasets in Ontario, Canada. Machine Learning approaches, including a Random Forests algorithm and the high-performance logistic regression, were used to derive parsimonious models to predict SSI status. Finally, a risk score methodology was used to transform the final models into the risk score system. The SSI risk models were validated in the validation datasets. Results Of 14,351 patients, 795 (5.5%) had an SSI. First, separate predictive models were built for three distinct administrative datasets. The final model, including hospitalization diagnostic, physician diagnostic and procedure codes, demonstrated excellent discrimination (C statistics, 0.91, 95% CI, 0.90–0.92) and calibration (Hosmer-Lemeshow χ2 statistics, 4.531, p = 0.402). Conclusion We demonstrated that health administrative data can be effectively used to identify SSIs. Machine learning algorithms have shown a high degree of accuracy in predicting postoperative SSIs and can integrate and utilize a large amount of administrative data. External validation of this model is required before it can be routinely used to identify SSIs.

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