Cancer is the most worried ailment as the percentage of cancer patients is increasing in huge numbers. The early diagnosis and prognosis of a cancer type plays an important role for the treatment and for clinical management of patient as well as its been important topic of research. For early detection, treatment and related recovery, examination of genes is important. Consequently, customized medicinal drug plays an essential component in treating the cancer. The personalized medicines are advised by means of studying the genetic profile of an individual with disorder. However, adopting personalized medicine in cancer treatment is happening slowly due to the big quantity of manual work is still required. There exist various algorithms like one hot encoding technique is used to extract features from genes and their variations, TF-IDF is used to extract features from the clinical text data. In order to increase the accuracy of the classification, algorithms like support vector machine, Naive Bayes, logistic regression and stacking model classifiers are tried in the proposed system.
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Y. Jahnavi, VIDISHA TIWARI
Dr. Y. Jahnavi received her BTech (CSE) degree from JNTUH, MTech (CSE) from Sri Venkateswara University, and Ph.D. (CSE) from GITAM University. She qualified GATE, APSET and NET examinations. Her interests include Data Science, Machine Learning, Natural Language Processing techniques etc. She has been publishing papers in national and international journals.
