BREAST CANCER CLASSIFICATION USING MACHINE LEARNING ALGORITHMS
1Ch. Kumudini Sreeja, P.Venkata Sireesha, R.Ramya Sri, Praveen Tumuluru
Cancer is the second reason behind death on the planet. Approximately eight million patients died because of cancer in 2019. The carcinoma is the leading reason for death amongst women. Several styles of studies are carried out on early detection of carcinoma to start remedy and growth the chance of survival. Most of the research concentrated on mammogram snapshots, MRI, and biopsy. However, mammograms, MRI, and biopsy photos have a risk of false detection that could endanger the patient's health. It is critical to hunt out alternative mechanisms that can be less complicated to implement and work with different information sets, which can be less expensive and safer, which may produce a greater dependable prediction. Classification, predictions are a form of the powerful processing strategies which are used to categorize and are expecting the records within the datasets, especially in a medical field, in which these strategies are widely utilized in prognosis and analysis to make decisions. The target of this paper is to match and perceive a correct model to predict the prevalence of carcinoma that supported various patient's medical records. The processing techniques make use of the gadget getting to know algorithms like a help vector device, naïve Bayes classifier, decision tree, Random Forest. It is anticipated that in actual application, physicians and patients can revel in the feature popularity outcome to prevent carcinoma, using these machines getting to know algorithms.
support vector machine, Naïve Bayes, Decision tree, Random Forest