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Uterine Cervix Cancer is one of the leading Cancer names effecting the female population worldwide [1] [2]. Incidence of Cervical Cancer can be reduced by 80% through a routine Pap smear test. Pap smear test requires skilled cytologists and is always prone inaccurate and inconsistent diagnosis due to manual error. Automated systems for easy recognition and proper staging of the cancerous cells can assists the medical professionals in correct diagnosis and planning of the proper treatment modality [3]. In research 23 well-known machine learning algorithms available in MatlabR2016 are extensively analyzed for their classification potential of Pap smear cases. To Train and Test the algorithms a huge database is created containing 8091 cervical cell imag pertaining to 200 clinical cases collected from three medical institutes of northern India. The raw cases of cervical cancer in form of Pap smear slides were photographed under a multi-headed digital microscope. After profiling the cells were vigilantly assigned classes by multiple cytotechnicians and histopathologists [4]. Cervical cases have seven classes of diagnosis [4].Quadratic SVM performed best among the 23 algorithms applied.

Analysis of Machine Learning and Statistics Tool Box Matlab R2016 over Novel Benchmark Cervical Cancer Database