• Divisha Khaturia
• Charu Khamesra
• Mayank Patel

Bioinformatics so far have become an integral part of the research and various other applications in big data. Determining the clinical implications through varied experiments in biomedical research is increasing rapidly. Selection of certain attributes on which the research will rely is yet another crucial task, as the outlook of the algorithm depicts how the information extracted is useful in the given field. Feature Selection so far has proved to be efficacious in this meadow of research, as it selects a subset of features for avoiding the issue of ‘curse of dimensionality’. Multi-attribute feature selection is chosen which involves the selection of relevant attributes from multi-attributed datasets, results of which improve accuracy. Our aim is toimitate the survival probability of the diabetic patients and identify the associated risk factors along with it. The analysis depicts the factors which contribute to a shorter survival span of diabetes mellitus patients. The factors include glucose levels, BMI, age, insulin, blood pressure, skin thickness and pregnancies.

Bioinformatics, Survival Probability, Diabetes Mellitus Patients, Feature Selection.

"Application of Feature Selection Techniques on Bioinformatics Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 9, page no.179-184, September-2018, Available :http://www.jetir.org/papers/JETIRB006035.pdf

"Application of Feature Selection Techniques on Bioinformatics Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 9, page no. pp179-184, September-2018, Available at : http://www.jetir.org/papers/JETIRB006035.pdf