Update default settings.

app/models/cancerpredictfields.rb

@@ -19,7 +19,7 @@ class Cancerpredictfields
 													   #3=>{:variable=>"Targeted_therapy",:name=>{"zh_tw"=>"標靶治療","en"=>"Targeted therapy"},:is_num=>0,:hint=>{'zh_tw'=>'抗HER2治療','en'=>''}, :comment_text=>{'zh_tw'=>'','en'=>''}, :choice_fields=> {"zh_tw"=>['否','是'],"en"=>['No','Yes']},:range=>[]}
   #}
   field :form_result_is_right , :type=> Integer ,default: 0
-  field :text_descibe ,type:Hash ,default:{"zh_tw"=>"歡迎使用台灣心血管疾病預後預測系統<br />本預測系統由全民健保資料庫2017年~2019年間共1964位病人電腦斷層影像所建立之預測模型<br />請在下方填入相關資訊","en"=>"Welcome to the Taiwan Breast Cancer Prediction System!<br/>The prediction system is constructed using clinical data from 90,841 breast cancer patients in the Taiwan Cancer Registry database between 2011 to 2015, and validated using clinical data from 49,374 breast cancer patients in the U.S.-based Surveillance, Epidemiology and End Results (SEER) database. <br/>To start, please select the information below."}
+  field :text_descibe ,type:Hash ,default:{"zh_tw"=>"歡迎使用台灣心血管疾病預後預測系統<br />本預測系統由全民健保資料庫2017年~2020年間共1964位病人電腦斷層影像所建立之預測模型<br />請在下方填入相關資訊","en"=>"Welcome to the Taiwan Breast Cancer Prediction System!<br/>The prediction system is constructed using clinical data from 90,841 breast cancer patients in the Taiwan Cancer Registry database between 2011 to 2015, and validated using clinical data from 49,374 breast cancer patients in the U.S.-based Surveillance, Epidemiology and End Results (SEER) database. <br/>To start, please select the information below."}
   field :small ,type:Hash ,default:{'font_size'=>"0.825em",'active'=>0}
   field :medium ,type:Hash ,default:{'font_size'=>"1em",'active'=>1}
   field :large ,type:Hash ,default:{'font_size'=>"1.25em",'active'=>0}
@@ -35,7 +35,62 @@ class Cancerpredictfields
   field :years ,type:Array ,default:[1,1.5,2,2.5]
   field :texts_between_Result_and_result_block ,type:Hash ,default:{'zh_tw'=>'如果欲將預測結果應用於臨床上，請務必與您的主治醫師討論後再做最後決定。','en'=>'Please note that the patients need to consult with their medical doctors before making any decision.'} 
   #field :image_uploader ,type:Object
-  field :prediction_formula , type: String ,default: ""
+  field :prediction_formula , type: String ,default: "A = 0.1327868* (sex_value- 0.4858824)
+
+       + 0.0371720* (age_test1 - 61.56000) -0.07447278* (age_test2 - 13.10152) 
+
+       + 0.4315686* (age_test3 -  0.9844332) 
+
+       + 0.0009163615*( calH_test1  - 182.9347) 
+
+       -0.0007536899*( calH_test2 - 124.8706) -0.00004697183*( calH_test3  -80.75636) 
+
+       + 0.0001401325*( calAH_test1  - 700.7824) 
+
+
+       -0.001349783*( calAH_test2 - 634.2167) +0.001753832*( calAH_test3  -419.3361) 
+
+    + 0.0001906046*( calDH_test1  -835.2894) -0.000251567*( calDH_test2 - 213.1630) 
+
+
+       -0.002173942*( fat_test1  -108.4149)+0.003066541*( fat_test2 - 28.33497) 
+
+      +0.6700708*(N4-0.3241176)
+
+      +0.3336162*(O3-0.4994118) 
+
+      +0.1322476*(O20-0.1741176) 
+
+      +0.9084972*(O18-0.008823529)
+
+      +0.2978388*(N12-0.1152941)
+
+      +0.1777935*(N20-0.3582353) 
+
+       +1.588042*(N31-0.002352941)  
+
+      +0.2197419*(O6-0.07823529) 
+
+      +1.791159*(N34-0.001176471) 
+
+      +0.4305973*(N14-0.02176471) 
+
+      -0.4472885*(N29-0.02411765) 
+
+      +0.2601319*(N26-0.04941176) 
+
+      -0.2364269*(O11-0.1164706) 
+
+      +0.1784179*(N6-0.1070588) 
+
+
+      +0.6023170*(O14-0.01294118) 
+
+      -1.031959*(N43-0.007058824) 
+
+      +0.4257809*(O17-0.01823529) 
+
+      +0.2002546*(O9-0.06176471)"
   field :years_settings , type: Array , default: ["0.8095037^( exp(A) )","0.729158^( exp(A) )","0.6717211^( exp(A) )","0.6056773^( exp(A) )"]
   field :mapping_data_from_csv , type: String ,default: ""
   scope :can_display, ->{where(:is_hidden=>false,:is_preview => false).any_of({:postdate.lt=>Time.now, :deadline.gt=>Time.now},{:postdate.lt=>Time.now, :deadline=>nil}).order_by([:is_top, :desc],[:postdate, :desc])}