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6.2 RELATED WORK 155

to common health check-ups and will minimize medical expenses. A training example was used to
prepare the random forest (RF), gradient-boosting decision tree and logistic regression were then ap-
plied to calculate the chances of hyperuricemia in the test dataset. Undersampling was applied to build
the prediction models to manage the cumbersome class dataset. The outcome showed that the RF had
the best performances in terms of sensitivity while the GBDT approach had the best performance in
specificity.
Diabetes mellitus is a disorder of glucose metabolism affecting the well-being of mankind all
around the world. Researchers are putting considerable effort into the various facets of diabetes, such
as its identification, complications, genetic conditions responsible, health impacts, and management.
Tao Zheng et al. [13] proposed a framework using machine learning that analyzes the computerized
health records to discover type 2 diabetes. Expert algorithms were employed for this purpose.
Classification models such as Naı ¨ve Bayes (NB), RF, SVM, and logistic regression were used to
model samples of cases and controls based on features obtained. The research work performed by
Md. Maniruzzaman et al. [14] includes utilization of machine learning methods for the grouping
of diabetes mellitus information. The process is based on a Gaussian classification technique with
three kernels adopted by them. They also compared the performance of a GP-based classification
method to existing techniques in terms of the accuracy, sensitivity, specificity, and positive and
negative predictive value.
M Alssema et al. [15] applied machine learning methods for identification and classification of di-
abetes mellitus patients, which throws light on the discrepancy observed in the distributions of max-
imum plantar pressure found in the diabetic population of an area, which helps to avoid foot ulcers
caused by diabetes. The SVM and dK-means clustering techniques were adopted for the diagnosis
of diabetes. Ioannis Kavakiotis et al. [16] reviewed the use of machine learning data mining method
tools in research carried out in diabetes. A number of machine learning techniques were used of which
most were supervised learning methods. Comparison of different machine learning algorithms in sev-
eral biological as well as clinical datasets was carried out. SVM showed the most excellent classifica-
tion accuracy.
Takemori Watanabe et al. [17] incorporates the various features obtained from functional connec-
tions or the network map at the resting stage of the entire brain with a multivariate technique. The reg-
ularization framework with 6D structure of the functional connection is taken into consideration by the
use of the fused Lasso through the hinge-loss generated a SVM with capability of feature selection.
Omar Y.Al-Jarrah et al. [18] provided an assessment of energy-efficient machine learning literatures.
They introduced a new perspective for technologists, analysts, and researchers in the computer science
and provide a layout for potential research activities. A distributed learning model for the restricted
Boltzmann machine (RBM) and the back-propagation algorithm using MapReduce was employed.
Theoretical and experimental aspects in large-scale data intensive fields, relating to model energy ef-
ficiency, including computational requirements in learning, and possible approaches, and structure and
design of data-intensive areas, including the relationship between data models and characteristics were
discussed.
Emmanuel Bibault, Philippe Giraud, and Anita Burgun [19] presented methods that could be used
to construct analytical models to treat cancer with radiation. Various machine learning methods, such
as SVM and artificial neural networks (NNs) were also integrated. Progresses in radiation oncology
have produced huge amounts of data that need to be incorporated. Electronic health reports also give
large volumes of information. With the advancement of big data analytics and machine learning, this