function BEST_DATA=COMB_classification(R,IHD,time_series_names,MD_DATA_D,MD_DATA_DD,MD_DATA,DESCRIPTORS,des_names)


num_params=size(R,2);
DATA=zeros(size(R,1),3);

include=zeros(size(R,2),1);

for par_idx=1:num_params
    
    X=R(:,par_idx);
    B = mnrfit(X,IHD+1);
    
    PHAT = mnrval(B,X);
    
    
    
    
    max_score1=0;
    max_score2=0;
    best_th=0;
    
    for class_th=.1:.01:0.5
        
        
        
        DATA(:,1)=str2double('0000');
        
        DATA(:,2)=PHAT(:,2);
        
        DATA(:,3)=PHAT(:,2)> class_th;
        
        DATA(DATA(:,2)<0.01,2)=0.01;
        DATA(DATA(:,2)>0.99,2)=0.99;
        
        
     %   if(~isempty(results))
            
            % Calculate sensitivity (Se) and positive predictivity (PPV)
            TP=sum(DATA(IHD==1,3));
            FN=sum(~DATA(IHD==1,3));
            FP=sum(DATA(IHD==0,3));
            Se=TP/(TP+FN);
            PPV=TP/(TP+FP);
            
            show=0; % if show is 1, the decile graph will be displayed by lemeshow()
            H=lemeshow([IHD DATA(:,2)],show);
            
            % Use the title of figure to display the results
            title(['H= ' num2str(H) ' Se= ' num2str(Se) ' PPV= ' num2str(PPV) '. ' num2str(class_th) ])
            
            % The event 1 score is the smaller of Se and PPV.
            score1 = min(Se, PPV);
            if score1>max_score1
                max_score1=score1;
                best_th=class_th;
                max_score2=H;
                %  display(['Unofficial Event 1 score: ' num2str(score1)]);
            end
            
       % end
        
    end
    
   survivals1=sum(X==1 & IHD==0);
   deaths1=sum(X==1 & IHD==1);
   survivals2=sum(X==0 & IHD==0);
   deaths2=sum(X==0 & IHD==1);
   
   if max_score1>0
       
      % include(par_idx)=1;
       
   disp(' ---')
   
   disp([' non NaNs:' num2str(deaths1/(deaths1+survivals1)) ])
   disp([' NaNs:' num2str(deaths2/(deaths2+survivals2)) ])
   
   ratio=(deaths1/(deaths1+survivals1)) / (deaths2/(deaths2+survivals2))
   
  name= time_series_names{par_idx}
  score= max_score1
   
   disp(' ---')
   
     if ratio >1.5 || ratio <0.6
       
       include(par_idx)=1;
     end
   
   end
    
end


num_params=size(R,2);
DATA=zeros(size(R,1),3);

%for par_idx=1:num_params


    X1=R(:,include==1);
    
    
    
    
        min_numdata1=min(sum(~isnan(MD_DATA_D),2));
    min_numdata2=min(sum(~isnan(MD_DATA_DD),2));
    
    
MD_DATA_D=MD_DATA_D(:,1:min_numdata1);
    MD_DATA_DD=MD_DATA_DD(:,1:min_numdata2);
    
     X2=[MD_DATA_D MD_DATA_DD MD_DATA];
     
     AGE_a=DESCRIPTORS(:,strcmp(des_names,'Age'));
    Gender_a=DESCRIPTORS(:,strcmp(des_names,'Gender'));
    ICUTYPE_a=[DESCRIPTORS(:,strcmp(des_names,'ICUType'))];

    ICUTYPE_t=ICUTYPE_a;

    ICUTYPE_a(ICUTYPE_t==1)=2;
    ICUTYPE_a(ICUTYPE_t==2)=4;
    ICUTYPE_a(ICUTYPE_t==3)=1;
    ICUTYPE_a(ICUTYPE_t==4)=3;

    X2=[AGE_a ICUTYPE_a X2];
    
    
    X=[X1 X2];
    
    
    %0.2708
    %X=[R(:,~strcmp(time_series_names,'MechVent'))];   
    
    %0.277
%     X=[R(:,strcmp(time_series_names,'TroponinT'))];    
%     X=[R(:,strcmp(time_series_names,'TroponinI')) X];
%     X=[R(:,strcmp(time_series_names,'RespRate')) X];
%     X=[R(:,strcmp(time_series_names,'Mg')) X];
%     X=[R(:,strcmp(time_series_names,'Lactate')) X];
%     X=[R(:,strcmp(time_series_names,'K')) X];
%     X=[R(:,strcmp(time_series_names,'FiO2')) X];
%     X=[R(:,strcmp(time_series_names,'Bilirubin')) X];
%     X=[R(:,strcmp(time_series_names,'AST')) X];
%     X=[R(:,strcmp(time_series_names,'ALT')) X];
%     X=[R(:,strcmp(time_series_names,'ALP')) X];
%     X=[R(:,strcmp(time_series_names,'Albumin')) X];
%     
    B = mnrfit(X,IHD+1);
    
    PHAT = mnrval(B,X);
    
    
    
    
    max_score1=0;
    max_score2=0;
    best_th=0;
    
    for class_th=.1:.01:0.5
        
        
        
        DATA(:,1)=str2double('0000');
        
        DATA(:,2)=PHAT(:,2);
        
        DATA(:,3)=PHAT(:,2)> class_th;
        
        DATA(DATA(:,2)<0.01,2)=0.01;
        DATA(DATA(:,2)>0.99,2)=0.99;
        
        
     %   if(~isempty(results))
            
            % Calculate sensitivity (Se) and positive predictivity (PPV)
            TP=sum(DATA(IHD==1,3));
            FN=sum(~DATA(IHD==1,3));
            FP=sum(DATA(IHD==0,3));
            Se=TP/(TP+FN);
            PPV=TP/(TP+FP);
            
            show=0; % if show is 1, the decile graph will be displayed by lemeshow()
            H=lemeshow([IHD DATA(:,2)],show);
            
            % Use the title of figure to display the results
            title(['H= ' num2str(H) ' Se= ' num2str(Se) ' PPV= ' num2str(PPV) '. ' num2str(class_th) ])
            
            % The event 1 score is the smaller of Se and PPV.
            score1 = min(Se, PPV);
            if score1>max_score1
                max_score1=score1;
                best_th=class_th;
                max_score2=H;
                BEST_DATA=DATA;
                %  display(['Unofficial Event 1 score: ' num2str(score1)]);
            end
            
       % end
        
    end
    
%    survivals1=sum(X==1 & IHD==0);
%    deaths1=sum(X==1 & IHD==1);
%    survivals2=sum(X==0 & IHD==0);
%    deaths2=sum(X==0 & IHD==1);
%    
%    disp([' non NaNs:' num2str(deaths1/(deaths1+survivals1)) ])
%    disp([' NaNs:' num2str(deaths2/(deaths2+survivals2)) ])
%    
   
   max_score1
   



end