Multilabelled Optimal Feature Classification Procedure for High Dimensional Bio Medical Data
G Vineela1, V Mnssvkr Gupta2

1G. Vineela, CST, S.R.K.R.E.C., Bhimavaram, India.
2V. MNSSVKR Gupta, CST, S.R.K.R.E.C., Bhimavaram, India. 

Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 262-266 | Volume-8 Issue-12, October 2019. | Retrieval Number: L37041081219/2019©BEIESP | DOI: 10.35940/ijitee.L3704.1081219
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Abstract: The Machine Learning field has extended its thrust virtually in any domain of analysis and within the near past has become a trusted tool in the medical domain. The experiential domain of automatic learning is employed in tasks like medical decision support, medical imaging, protein-protein interaction, extraction of medical data, and for overall patient management care. ML is pictured as a tool by that computer-based systems are often integrated within the health care field so as to induce a far better, well-organized treatment. To extract optimal feature selection with high dimensional bio-medical knowledge, during this paper propose a Advance Machine Learning Approach with optimization approach i.e. Ant Colony Optimization (ACO). It extracts sentences from revealed medical papers that mention diseases and coverings, and identifies semantic relations that exit between diseases and coverings. Our analysis results for these tasks show that the projected methodology obtains reliable outcomes that might be integrated in associative application to be employed in the treatment domain.
Keywords: Healthcare, Machine Learning, Natural Language Processing, Support Vector Machine, Decision Support System.
Scope of the Article: Natural Language Processing