TITLE: Multiclass Disease Predictions Based on Integrated Clinical and Genomics Datasets 

ABSTRACT: Clinical predictions using clinical data by computational methods are common
in bioinformatics. However, clinical predictions using information from
genomics datasets as well is not a frequently observed phenomenon in research.
Precision medicine research requires information from all available datasets to
provide intelligent clinical solutions. In this paper, we have attempted to
create a prediction model which uses information from both clinical and
genomics datasets. We have demonstrated multiclass disease predictions based on
combined clinical and genomics datasets using machine learning methods. We have
created an integrated dataset, using a clinical (ClinVar) and a genomics (gene
expression) dataset, and trained it using instance-based learner to predict
clinical diseases. We have used an innovative but simple way for multiclass
classification, where the number of output classes is as high as 75. We have
used Principal Component Analysis for feature selection. The classifier
predicted diseases with 73\% accuracy on the integrated dataset. The results
were consistent and competent when compared with other classification models.
The results show that genomics information can be reliably included in datasets
for clinical predictions and it can prove to be valuable in clinical
diagnostics and precision medicine.
Clinical predictions using clinical data by computational methods are common
in bioinformatics. However, clinical predictions using information from
genomics datasets as well is not a frequently observed phenomenon in research.
Precision medicine research requires information from all available datasets to
provide intelligent clinical solutions. In this paper, we have attempted to
create a prediction model which uses information from both clinical and
genomics datasets. We have demonstrated multiclass disease predictions based on
combined clinical and genomics datasets using machine learning methods. We have
created an integrated dataset, using a clinical (ClinVar) and a genomics (gene
expression) dataset, and trained it using instance-based learner to predict
clinical diseases. We have used an innovative but simple way for multiclass
classification, where the number of output classes is as high as 75. We have
used Principal Component Analysis for feature selection. The classifier
predicted diseases with 73\% accuracy on the integrated dataset. The results
were consistent and competent when compared with other classification models.
The results show that genomics information can be reliably included in datasets
for clinical predictions and it can prove to be valuable in clinical
diagnostics and precision medicine.
Multiclass Disease Predictions Based on Integrated Clinical and Genomics Datasets
http://arxiv.org/abs/2006.07879v1