chander Mr. A. Harish
<span title="2020-02-17">2020</span> <i title="International Journal of Pharma and Bio Sciences"> <a target="_blank" rel="noopener" href="" style="color: black;">International Journal of Pharma and Bio Sciences</a> </i> &nbsp;
about this Special Issue This special issue is highly related to Microbiology and Bioinformatics application for pharmacology and its usage in medical and health care systems towards providing leads for molecular development and an initiative in Basic science to perform advanced research in areas like the virtual patient and CRISPER Human. Also, it focuses on the integration of machine learning algorithms to mankind. In addition to the basic science involved in Microbiology, Bioinformatics and
more &raquo; ... harmacology, the advancements in the current stage of applying the schemas of artificial intelligence (AI) in basic sciences are also focused in this issue. Artificial intelligence in the diagnosis of health care is the requirement for the current era of Digital Genomics. This special issue focuses on both basic science and advanced developments in the current era of Digital Genomics. The articles published in this special issue will certainly bring a positive effect for the development of health care systems and scientific leads to develop molecules with the available resources enhance the maximum utilization of scientific knowledge to potentiate diagnosis and therapy in health care. ABSTRACT The Random Forest Algorithm is an Ensemble learning method for classification or regression problems. It can be used for building predictive problems. In recent years, Chronic Diabetes Disease is keeping on increasing. There are many reasons like changes in our life style, our food habits. It roots an increase in blood sugar levels. If Diabetes Disease remains untreated or unidentified many different types of other problems may be happened. The doctors are difficult to identify these kinds of diseases easily. The machine learning algorithms support the doctor to solve these types of problems. In this paper, we implemented the Random Forest algorithm to predict diabetes disease at an early stage. Experiments are performed using the R tool on Pima Indians Diabetes Dataset which is from the UCI machine learning repository. The performance of the algorithm is evaluated using measures on Accuracy. Results obtained in Random Forest Algorithm displays accuracy is 80.08%.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.22376/ijpbs/ijlpr/sp08/jan/2020.1-222</a> <a target="_blank" rel="external noopener" href="">fatcat:ovsvmyi76ret5pnyfin443lhii</a> </span>
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