Accuracy Analysis of K-Nearest Neighbor and Naïve Bayes Algorithm in the Diagnosis of Breast Cancer

Abstract

In the medical field, there are many records of disease sufferers, one of which is data on breast cancer. An extraction process to fine information in previously unknown data is known as data mining. Data mining uses pattern recognition techniques such as statistics and mathematics to find patterns from old data or cases. One of the main roles of data mining is classification. In the classification dataset, there is one objective attribute or it can be called the label attribute. This attribute will be searched from new data on the basis of other attributes in the past. The number of attributes can affect the performance of an algorithm. This results in if the classification process is inaccurate, the researcher needs to double-check at each previous stage to look for errors. The best algorithm for one data type is not necessarily good for another data type. For this reason, the K-Nearest Neighbor and Naïve Bayes algorithms will be used as a solution to this problem. The research method used was to prepare data from the breast cancer dataset, conduct training and test the data, then perform a comparative analysis. The research target is to produce the best algorithm in classifying breast cancer, so that patients with existing parameters can be predicted which ones are malignant and benign breast cancer. This pattern can be used as a diagnostic measure so that it can be detected earlier and is expected to reduce the mortality rate from breast cancer. By making comparisons, this method produces 95.79% for K-Nearest Neighbor and 93.39% for Naïve Bayes

A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

“…As shown by [17], [18], the KNN algorithm is used to identify heart disease. According to [10], NB and KNN are used in the diagnosis of breast cancer. In another paper [19], behavior determinant-based cervical cancer early detection using NB and LR.…”

“…Several classifying algorithms have been used in the analysis of medical diagnosis without feature selection. They include support vector machines (SVMs) used by [3], [4] random forests (RF) in [5], [6], k nearest neighbors (KNN) used by [7], [8], decision trees (DT) exploited in [7], [9], naive Bayes (NB) in [10], and logistics regression (LR) in [11]. Even though many studies have been done and reported in the literature, there is still room for improvement.…”

Metode K-Nearest Neighbor Dan Fitur Warna Untuk Klasifikasi Daun Sirih Berdasarkan Citra Digital

“…F. Klasifikasi Klasifikasi adalah salah satu topik data mining yang paling populer untuk mencari informasi dan banyak digunakan untuk menentukan suatu keputusan dengan pengetahuan baru yang diperoleh dari pengolahan data masa lalu menggunakan suatu algoritma (Ananda & Prasetiadi, 2021;Handayani & Ikrimach, 2020;Nuklianggraita et al, 2020) Pada penelitian ini, proses klasifikasi dilakukan dengan menggunakan cross-validation untuk membagi dataset menjadi dua set yaitu data training dan data testing serta menerapkan metode klasifikasi KNN.…”

COMPATIBILITY OF SELECTION OF STUDENT DEPARTMENTS USING k-NEAREST NEIGHBOR AND NAÏVE BAYES CLASSIFIER IN INFORMATICS PRIVATE VOCATIONAL SCHOOL, SERANG CITY

“…Further research has also been carried out by [6], [7] who conducted research on the comparative analysis of the naïve Bayes Classifier and K-Nearest Neighbor methods against data classification, with the Naive Bayes method, the k-nn algorithm, confusion matrix, with the results of the comparison of the two methods. It can be concluded that the k-NN method has better accuracy than the NBC method.…”