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MisclassifiedPatient.m
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MisclassifiedPatient.m
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function MisclassifiedPatient(cmd,ind,Patients_Name,p,mi)
%
% MisclassifiedPatient: Display a list of misclassified patients and their
% corresponding neighbors and relative distance from neighboring data
% points (Patients)
%
% Syntax
% MisclassifiedPatient function is not called independently but is
% called from MultilayerNetwork window, which passes misclassification
% matrix after training
%
%
% Description
%
% Milind Malshe
% DEFAULTS
if nargin == 0,
cmd = '';
ind = '';
else
cmd = lower(cmd);
end
% FIND WINDOW IF IT EXISTS
fig = 0;
% We alow the program to see hidden handles
fig=findall(0,'type','figure','tag','MisclassifiedPatient');
if (size(fig,1)==0),
fig=0;
end
if (length(get(fig,'children')) == 0),
fig = 0;
end
% GET WINDOW DATA IF IT EXISTS
if fig
H = get(fig,'userdata');
end
%==================================================================
% Activate the window.
%
% ME() or ME('')
%==================================================================
if strcmp(cmd,'') | isempty(cmd)
if fig
figure(fig)
set(fig,'visible','on')
else
MisclassifiedPatient('init',ind);
end
%==================================================================
% Close the window.
%
% ME() or ME('')
%==================================================================
elseif strcmp(cmd,'close') & (fig)
if exist(cat(2,tempdir,'MisclassifiedPatient.mat'))
delete(cat(2,tempdir,'MisclassifiedPatient.mat'));
end
delete(fig);
elseif strcmp(cmd,lower('thresholdMiscalssPercent')) & (fig)
misclassPerc = str2num(H.thresholdMisclassPercent_popUp_String(get(H.thresholdMisclassPercent_popUp,'Value'),:));
misclassPerc = misclassPerc/100;
% select_misClassPatient = get(H.misclassPatient_listBox,'Value');
% [ind] = findMisclass(size(H.data.p,2),H.data.mi(select_misClassPatient),misclassPerc)
[ind] = findMisclass(size(H.data.p,2),H.data.mi,misclassPerc);
% data.ind = ind;
num_misClass = size(ind,2);
if (num_misClass ~= 0)
% set(H.misclassPatient_listBox,'String',misClassPatient);
% data.misclassPatient = misClassPatient;
for i = 1:num_misClass
misClassPatient{i} = H.data.Patients_Name{ind(i)};
end
set(H.misclassPatient_listBox,'String',misClassPatient);
% Find the five neighbors of the most misclassified healthy patient
%%%%%MILIND ********** 11_02_07
%%%%% if the number training data points is less than 5, then it finds
%%%%% nearest neighbors from the entire data set which would be less than 5.
% num_FindNeighbors = 5;
num_FindNeighbors = min( (size(H.data.p,2)-1), 5);
%%%%%MALSHE ********** 11_02_07
[ind2,rel_dis] = findNeighbors(H.data.p,H.data.ind(1),num_FindNeighbors);
numNeighbors = size(ind2,1);
for i = 1:numNeighbors
neighborsDist{i} = sprintf('%s%s%s%90s','',H.data.Patients_Name{ind2(i)},...
'', ...
num2str(rel_dis(i)));
end
set(H.nearestNeighborDist_listBox,'String',neighborsDist);
set(H.thresholdMisclassPercent_popUp,'Userdata',(misclassPerc*100));
else
warndlg('There are no misclassified patients at the selected threshold');
temp = get(H.thresholdMisclassPercent_popUp,'Userdata');
am = find((str2num(H.thresholdMisclassPercent_popUp_String) == temp));
set(H.thresholdMisclassPercent_popUp,'Value',am);
% MultiLayerNetwork();
% MisclassrifiedPatient('close');
return;
end
%======= End of updating Miscalssified Patients listBox using Threshold Misclassification Percentage =======
elseif strcmp(cmd,lower('misclassPatient_listBox')) & (fig)
select_misclassPatient = get(H.misclassPatient_listBox,'Value');
% Find the five neighbors of the most misclassified healthy patient
%%%%%MILIND ********** 11_02_07
%%%%% if the number training data points is less than 5, then it finds
%%%%% nearest neighbors from the entire data set which would be less than 5.
% num_FindNeighbors = 5;
num_FindNeighbors = min( (size(H.data.p,2)-1), 5);
%%%%%MALSHE ********** 11_02_07
[ind2,rel_dis] = findNeighbors(H.data.p,H.data.ind(select_misclassPatient),num_FindNeighbors);
numNeighbors = size(ind2,1);
for i = 1:numNeighbors
neighborsDist{i} = sprintf('%s%s%s%70s','',H.data.Patients_Name{ind2(i)},...
'', ...
num2str(rel_dis(i)));
end
set(H.nearestNeighborDist_listBox,'String',neighborsDist);
elseif strcmp(cmd,lower('display_feature')) & (fig)
fig_data = get(fig,'Userdata');
features = fig_data.data.p;
select_misclassPatientName = fig_data.data.misclassPatient;
select_misclassPatient = select_misclassPatientName{get(H.misclassPatient_listBox,'Value')};
select_misclassPatientName_NeighborName = get(H.nearestNeighborDist_listBox,'String');
select_nearestNeighborDist = select_misclassPatientName_NeighborName{get(H.nearestNeighborDist_listBox,'Value')};
space = strfind(select_nearestNeighborDist,' ')
select_nearestNeighbor = select_nearestNeighborDist(1:space-1);
% select_features = get(H.feature_listBox,'Value');
num_select_features = size(features,2);
num_select_Patients = 2;
selectPatients = {select_misclassPatient,select_nearestNeighbor};
if num_select_Patients~=0,
j = 1;
for i = 1:size(selectPatients,2)
pp{i} = fig_data.data.p(:,find(strcmp((fig_data.data.Patients_Name),(selectPatients{i}))));
info{j}.Lead = selectPatients{i};
info{j}.fs = 1;
j = j+1;
end
SAIDplot(pp,[],info,'Feature Index Number','Features for Selected Patients');
% SAIDplot(p',[]);
end
%==================================================================
% Initialize the window.
%
% ME('init')
%==================================================================
elseif (strcmp(cmd,'init') & (~fig))
window_en = 'on';
window_dis = 'off';
uipos = getuipos;
% default = getdefaults;
H.StdColor = get(0,'DefaultUicontrolBackgroundColor');
H.StdUnit='character';
H.PointsToPixels = 72/get(0,'ScreenPixelsPerInch');
H.me='MisclassifiedPatient';
fig = figure('Units',H.StdUnit, ...
'Color',[0.8 0.8 0.8], ...
'IntegerHandle', 'off',...
'Interruptible','off', ...
'BusyAction','cancel', ...
'HandleVis','Callback', ...
'MenuBar','none', ...
'Name',H.me, ...
'Numbertitle','off', ...
'Units', H.StdUnit, ...
'PaperUnits','points', ...
'Position',uipos.fig, ...
'Tag','MisclassifiedPatient', ...
'Resize','off', ...
'ToolBar','none');
H.text1 = uicontrol('Parent',fig, ...
'Units',H.StdUnit, ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Enable',window_en, ...
'ListboxTop',0, ...
'Position',uipos.text1, ...
'String','Misclassified Patients', ...
'FontSize', 14,...
'Style','text', ...
'Tag','text1');
H.frameMisclassPatient = uicontrol('Parent',fig, ...
'Units',H.StdUnit, ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',uipos.frameMisclassPatient, ...
'Style','frame', ...
'Tag','frameMisclassPatient');
H.frameNearestNeighborDist = uicontrol('Parent',fig, ...
'Units',H.StdUnit, ...
'BackgroundColor',[0.8 0.8 0.8], ...
'ListboxTop',0, ...
'Position',uipos.frameNearestNeighborDist, ...
'Style','frame', ...
'Tag','frameNearestNeighborDist');
H.misclassPatient_listBox = uicontrol('Parent',fig, ...
'Units',H.StdUnit, ...
'Callback','MisclassifiedPatient(''misclassPatient_listBox'')', ...
'BackgroundColor',[1 1 1], ...
'Position',uipos.misclassPatient_listBox, ...
'Style','Listbox', ...
'Tag','misclassPatient_listBox', ...
'ToolTipStr','Misclassified patients: sorted in descending order, with the patient being mislassified the most number of times on top', ...
'Max',1, ...
'Value',1);
H.nearestNeighborDist_listBox = uicontrol('Parent',fig, ...
'Units',H.StdUnit, ...
'BackgroundColor',[1 1 1], ...
'Position',uipos.nearestNeighborDist_listBox, ...
'Style','Listbox', ...
'Tag','nearestNeighborDist_listBox', ...
'ToolTipStr','Patients with input features close to the selected misclassified patient', ...
'Max',1, ...
'Value',1);
H.textMisclassPatient = uicontrol('Parent',fig, ...
'Units',H.StdUnit, ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Enable',window_en, ...
'ListboxTop',0, ...
'Position',uipos.textMisclassPatient, ...
'String','Misclassified Patients', ...
'Style','text', ...
'Tag','textMisclassPatient');
H.textNearestNeighbor = uicontrol('Parent',fig, ...
'Units',H.StdUnit, ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Enable',window_en, ...
'ListboxTop',0, ...
'Position',uipos.textNearestNeighbor, ...
'String','Nearest Neighbors', ...
'Style','text', ...
'Tag','textNearestNeighbor');
H.textDist = uicontrol('Parent',fig, ...
'Units',H.StdUnit, ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Enable',window_en, ...
'ListboxTop',0, ...
'Position',uipos.textDist, ...
'String','Relative Distance', ...
'Style','text', ...
'Tag','textDist');
% H.dist_listBox = uicontrol('Parent',fig, ...
% 'Units',H.StdUnit, ...
% 'BackgroundColor',[1 1 1], ...
% 'Position',uipos.dist_listBox, ...
% 'Style','Listbox', ...
% 'Tag','dist_listBox', ...
% 'ToolTipStr','', ...
% 'Max',1, ...
% 'Value',1);
H.textThresholdMisclassPercent_popUp = uicontrol('Parent',fig, ...
'Units',H.StdUnit, ...
'BackgroundColor',[0.8 0.8 0.8], ...
'Enable',window_en, ...
'ListboxTop',0, ...
'Position',uipos.textThresholdMisclassPercent_popUp, ...
'String','Threshold Misclassification Percentage', ...
'Style','text', ...
'Tag','textThresholdMisclassPercent_popUp');
H.thresholdMisclassPercent_popUp_String = [' 5';'10';'20';'30';'40';'50';'60';'70';'80';'90'];
H.thresholdMisclassPercent_popUp = uicontrol('Parent',fig, ...
'Units',H.StdUnit, ...
'Callback','MisclassifiedPatient(''thresholdMiscalssPercent'')', ...
'BackgroundColor',[1 1 1], ...
'Enable',window_en, ...
'ListboxTop',0, ...
'Position',uipos.thresholdMisclassPercent_popUp, ...
'Max',1, ...
'String',H.thresholdMisclassPercent_popUp_String, ...
'Style','popupmenu', ...
'ToolTipStr','Set a threshold percentage for misclassified patients', ...
'Tag','thresholdMisclassPercent_popUp', ...
'Value',1, ...
'Userdata',5);
% H.textDisplay = uicontrol('Parent',fig, ...
% 'Units',H.StdUnit, ...
% 'BackgroundColor',[0.8 0.8 0.8], ...
% 'Enable',window_en, ...
% 'ListboxTop',0, ...
% 'Position',uipos.textDisplay, ...
% 'String','Display/Analysis', ...
% 'Style','text', ...
% 'Tag','textDisplay');
%
%
%
%
% H.inputSens_button = uicontrol('Parent',fig, ...
% 'Units',H.StdUnit, ...
% 'Callback','SAIDutil(''MultiLayerNetwork'',''inputSens'')', ...
% 'Enable','off', ...
% 'ListboxTop',0, ...
% 'Position',uipos.inputSens_button, ...
% 'String','Input Sensitivity', ...
% 'ToolTipStr','Display the sesitivity of the target for each of the input variable',...
% 'Tag','close_button');
%
% H.commentFrame = uicontrol('Parent',fig, ...
% 'Units',H.StdUnit, ...
% 'BackgroundColor',[0.8 0.8 0.8], ...
% 'ListboxTop',0, ...
% 'Position',uipos.commentFrame, ...
% 'Style','frame', ...
% 'Tag','commentFrame');
%
%
%
% H.commentTextString = 'Set neural network training parameters';
% H.commentText = uicontrol('Parent',fig, ...
% 'Units',H.StdUnit, ...
% 'BackgroundColor',[0.8 0.8 0.8], ...
% 'Enable',window_en, ...
% 'ListboxTop',0, ...
% 'Position',uipos.commentText, ...
% 'String',H.commentTextString, ...
% 'FontWeight','bold', ...
% 'ForegroundColor',[0 0 1], ...
% 'Style','text', ...
% 'Tag','StaticText1');
H.close_button = uicontrol('Parent',fig, ...
'Units',H.StdUnit, ...
'Callback','MisclassifiedPatient(''Close'')', ...
'Enable','on', ...
'ListboxTop',0, ...
'Position',uipos.close_button, ...
'String','Close', ...
'ToolTipStr','Exit Misclassified Patients window and return to Multilayer Network',...
'Tag','close_button');
H.display_EKGsignal_button = uicontrol('Parent',fig, ...
'Units',H.StdUnit, ...
'Callback','', ...
'Enable','off', ...
'ListboxTop',0, ...
'Position',uipos.display_EKGsignal_button, ...
'String','Display EKG signal', ...
'ToolTipStr','Display EKG signal',...
'Tag','display_EKGsignal_button');
H.display_feature_button = uicontrol('Parent',fig, ...
'Units',H.StdUnit, ...
'Callback','MisclassifiedPatient(''display_feature'')', ...
'Enable','on', ...
'ListboxTop',0, ...
'Position',uipos.display_feature_button, ...
'String','Display features', ...
'ToolTipStr','Display features: Select 1 misclassified patient and 1 patient from its nearest neighbors with input features close to it',...
'Tag','display_feature_button');
data.ind = ind;
data.Patients_Name = Patients_Name;
data.p = p;
data.mi = mi;
num_misClass = size(data.ind,2);
for i = 1:num_misClass
misClassPatient{i} = data.Patients_Name{data.ind(i)};
end
if (num_misClass ~= 0)
set(H.misclassPatient_listBox,'String',misClassPatient);
data.misclassPatient = misClassPatient;
else
warndlg('There are no misclassified patients');
MultiLayerNetwork();
MisclassifiedPatient('close');
return;
end
% Find the five neighbors of the most misclassified healthy patient
%%%%%MILIND ********** 11_02_07
%%%%% if the number training data points is less than 5, then it finds
%%%%% nearest neighbors from the entire data set which would be less than 5.
% num_FindNeighbors = 5;
num_FindNeighbors = min( (size(data.p,2)-1), 5);
%%%%%MALSHE ********** 11_02_07
[ind2,rel_dis] = findNeighbors(data.p,data.ind(1),num_FindNeighbors);
numNeighbors = size(ind2,1);
for i = 1:numNeighbors
neighborsDist{i} = sprintf('%s%s%s%70s','',data.Patients_Name{ind2(i)},...
'', ...
num2str(rel_dis(i)));
end
set(H.nearestNeighborDist_listBox,'String',neighborsDist);
H.data = data; %%%%%MILIND MALSHE
set(fig,'userdata',H);
end
% %=========== End of MisclassifiedPatient =============
function uipos = getuipos
border = 1.2;
button_gapW = 3;
button_gapH = 1;
mode_buttonW = 22;
mode_buttonH = 1.6;
action_buttonW = 18;
action_buttonH = 1.6;
textRadioButtonNameW = 45;
textRadioButtonW = 10;
textRadioButtonH = 1.4;
textListBoxW = 22;
textListBoxH = 1.2;
listBoxW = 55;
listBoxH = 25;
popUpMenuW = 14;
popUpMenuH = 1;
commentTextH = 2.0;
text1H = 2;
% gapBetween2Frames = button_gapW/2;
figW = 2*border + listBoxW + 2*border + border + listBoxW + 0.5*listBoxW + 2*border ;
figH = 2*border + mode_buttonH + 2*border + action_buttonH + listBoxH + textListBoxH + 2*border + popUpMenuH + 1*border + text1H + 2*border;
sunits = get(0, 'Units');
set (0, 'Units', 'character');
ssinchar = get(0, 'ScreenSize');
set (0, 'Units', sunits);
figL = (ssinchar(3) - figW) / 2;
figB = (ssinchar(4) - figH) / 2;
uipos.fig = [figL, figB, figW, figH];
text1W = figW - 20*border;
text1L = figW/2 - text1W/2;
text1B = figH - 2*border - text1H + 1* commentTextH/2;
uipos.text1 = [text1L, text1B, text1W, text1H];
frameMisclassPatientL = border;
frameMisclassPatientB = 1*border + mode_buttonH + action_buttonH + 2*border;
frameMisclassPatientW = listBoxW + 2*border;
frameMisclassPatientH = listBoxH + 2*border;
uipos.frameMisclassPatient = [frameMisclassPatientL,frameMisclassPatientB,frameMisclassPatientW,frameMisclassPatientH];
frameNearestNeighborDistL = frameMisclassPatientL + frameMisclassPatientW + border;
frameNearestNeighborDistB = frameMisclassPatientB;
frameNearestNeighborDistW = 1.5*listBoxW + 2*border;
frameNearestNeighborDistH = listBoxH + 2*border;
uipos.frameNearestNeighborDist = [frameNearestNeighborDistL,frameNearestNeighborDistB,frameNearestNeighborDistW,frameNearestNeighborDistH];
misclassPatient_listBoxL = frameMisclassPatientL + border;
misclassPatient_listBoxB = frameMisclassPatientB + border/2;
uipos.misclassPatient_listBox = [misclassPatient_listBoxL,misclassPatient_listBoxB,listBoxW,(listBoxH+border)];
nearestNeighborDist_listBoxL = frameNearestNeighborDistL + border;
nearestNeighborDist_listBoxB = frameNearestNeighborDistB + border/2;
nearestNeighborDist_listBoxW = 1.5*listBoxW;
nearestNeighborDist_listBoxH = listBoxH + border;
uipos.nearestNeighborDist_listBox = [nearestNeighborDist_listBoxL,nearestNeighborDist_listBoxB,nearestNeighborDist_listBoxW,nearestNeighborDist_listBoxH];
% dist_listBoxL = nearestNeighbor_listBoxL + listBoxW;
% dist_listBoxB = nearestNeighbor_listBoxB;
% uipos.dist_listBox = [dist_listBoxL,dist_listBoxB,listBoxW,(listBoxH+border)];
textMisclassPatientL = misclassPatient_listBoxL + listBoxW/2 - textListBoxW/2;
textMisclassPatientB = frameMisclassPatientB + frameMisclassPatientH - textListBoxH/2;
uipos.textMisclassPatient = [textMisclassPatientL, textMisclassPatientB, textListBoxW, textListBoxH];
textNearestNeighborL = nearestNeighborDist_listBoxL + listBoxW/2 - textListBoxW;
textNearestNeighborB = frameNearestNeighborDistB + frameNearestNeighborDistH - textListBoxH/2;
uipos.textNearestNeighbor = [textNearestNeighborL,textNearestNeighborB,textListBoxW, textListBoxH];
textDistL = nearestNeighborDist_listBoxL + listBoxW ;
textDistB = frameNearestNeighborDistB + frameNearestNeighborDistH - textListBoxH/2;
uipos.textDist = [textDistL,textDistB,textListBoxW, textListBoxH];
textThresholdMisclassPercent_popUpL = 40*border;
textThresholdMisclassPercent_popUpB = frameMisclassPatientB + frameMisclassPatientH + 2*border;
uipos.textThresholdMisclassPercent_popUp = [textThresholdMisclassPercent_popUpL,textThresholdMisclassPercent_popUpB,textRadioButtonNameW, textRadioButtonH];
thresholdMisclassPercent_popUpL = textThresholdMisclassPercent_popUpL + textRadioButtonNameW;
thresholdMisclassPercent_popUpB = textThresholdMisclassPercent_popUpB + popUpMenuH;
uipos.thresholdMisclassPercent_popUp = [thresholdMisclassPercent_popUpL,thresholdMisclassPercent_popUpB,popUpMenuW, popUpMenuH];
close_buttonL = figW - 2*border - action_buttonW;
close_buttonB = border;
uipos.close_button = [close_buttonL, close_buttonB, action_buttonW, action_buttonH ];
display_EKGsignal_buttonL = figW/2 - button_gapW/2 - mode_buttonW;
display_EKGsignal_buttonB = close_buttonB + action_buttonH + border;
uipos.display_EKGsignal_button = [display_EKGsignal_buttonL, display_EKGsignal_buttonB, mode_buttonW, mode_buttonH ];
display_feature_buttonL = figW/2 + button_gapW;
display_feature_buttonB = display_EKGsignal_buttonB;
uipos.display_feature_button = [display_feature_buttonL, display_feature_buttonB, mode_buttonW, mode_buttonH ];
commentFrameL = border;
commentFrameB = 0.8* border;
commentFrameW = display_EKGsignal_buttonL - border - button_gapW;
commentFrameH = 2;
uipos.commentFrame = [commentFrameL, commentFrameB, commentFrameW, commentFrameH];
commentTextL = commentFrameL + border;
commentTextB = commentFrameB + 0.2* border;
commentTextW = commentFrameW - 4* border;
commentTextH = 0.8* commentFrameH;
uipos.commentText = [commentTextL, commentTextB, commentTextW, commentTextH];