heartc                 package:dprep                 R Documentation

_T_h_e _H_e_a_r_t _C_l_e_v_e_l_a_n_d _d_a_t_a_s_e_t

_D_e_s_c_r_i_p_t_i_o_n:

     This dataset contains information concerning heart disease
     diagnosis.  The data was collected from the  Cleveland Clinic
     Foundation, and it is available  at the UCI machine learning
     Repository. Six instances containing missing values  have been
     deleted from the original dataset.

_U_s_a_g_e:

     data(heartc)

_F_o_r_m_a_t:

     A data frame with 297 observations on the following 14 variables.

     _V_1 age(continuous)

     _V_2 sex

     _V_3 cp, chest pain type:1,2,3,4

     _V_4 trestbps: resting blood pressure(continuous)

     _V_5 cholesterol(continuous)

     _V_6 fps: fatsing blood sugar>120? yes=1, no =0

     _V_7 restecg: resting electrocardiographic results, 0,1, 2

     _V_8 thalach: maximum heart rate achieved(continuous) 

     _V_9 exang: exercise induced angina (1 = yes; 0 = no)

     _V_1_0 oldpeak = ST depression induced by exercise relative to rest
          (continuous)

     _V_1_1 slope: the slope of the peak exercise ST segment

     _V_1_2 ca: number of major vessels (0-3) colored by flourosopy

     _V_1_3 thal: 3 = normal; 6 = fixed defect; 7 = reversable defect

     _V_1_4 diagnosis of heart disease: 1: < 50 2: > 50

_D_e_t_a_i_l_s:

     Six instances containing missing values have been deleted from the
     original dataset.  This dataset includes continuous, binomial,
     nominal, and ordinal features.

_S_o_u_r_c_e:

     The UCI Machine Learning Database Repository at:

        *  <URL: ftp://ftp.ics.uci.edu/pub/machine-learning-databases>

        *  <URL: http://www.ics.uci.edu/~mlearn/MLRepository.html>

_E_x_a_m_p_l_e_s:

     #----Detecting outliers using the Relief---
     data(heartc)
     relief(heartc,200,0.4,vnom=c(2,3,6,7,9,11:13))

