### I. Introduction

### II. Methods

### 1. Data Collection

### 2. Prognostic Factor Selection

*p*< 0.05) variables were histological grade, local invasion of tumor, number of tumors, tumor size, lymphovascular invasion, estrogen-receptor status, and the number of metastatic lymph nodes. A prediction model for breast cancer recurrence within 5 years after surgery was then constructed using a naïve Bayesian classifier. The model was transformed into a nomogram for representation on paper.

### 3. Naïve Bayesian Nomogram

*P*(

*c*|

*X*) for a sample X with a set of instance

*X*= (

*a*

_{1},

*a*

_{2}, ...,

*a*

_{m}) to be a member of class c is computed as follows:

*c*be the class for which a nomogram is constructed; the probability of the alternative class c is

*P*(

*c*|

*X*).

*Odds*for these two probabilities are defined as

*it*is defined as log

*Odds*. This translates to

*OR*is the odds ratios. Here, log

*it*of class probability

*P*(

*c*|

*X*) is determined by the sum of independent values of logOR of the attribute value (

*a*

_{i}). This property is used for the construction of a nomogram that relates the feature values to the point score. Experts, such as statisticians, can interpret a scale using log

*OR*points, but most clinicians may find it easier to use a scale with integer points from 0 to 100. Therefore, we used integer points in deference to clinical environments. To show the sum of individual point scores as a class probability, we start from equation (3) and

*F*(

*c*|

*X*). Let

*F*(

*c*|

*X*) equal the sum of log

*OR*(

*a*

_{i}). Equation (3) becomes and

*P*(

*c*|

*X*) is computed as

*P*(

*c*|

*X*) =

*f*(

*F*(

*c*|

*X*)). It is linked by the known attributes to the class probability. This nomogram is a graphical calculating representation that is used by the total points to identify the probability of breast cancer recurrence within 5 years after breast cancer surgery. The naive Bayes classifier and nomogram visualization was implemented in the Orange data mining suite (University of Ljubljana, Ljubljana, Slovenia). The naïve Bayes classifier uses locally weighted scatterplot smoothing (LOESS) to estimate the conditional probabilities for continuous attributes. The size of the LOESS window and the LOESS sample points are 0.5 and 100, respectively. Figure 2 shows the proposed nomogram based on the constructed naïve Bayesian classifier.