}//End of ClassifyPre
public ActionResult ClassifyPost(string className, int classID)
{
var classModel = new ClassificationChoicePost {
choiceName = className, choiceID = classID
};
return(View(classModel));
}//End of ClassifyPost
public ActionResult ResultsPost(ClassificationChoicePost model)
{
ML_DBEntities dbML = new ML_DBEntities();
//Normalize Necessary Values
model.radius = (model.radius - 6.981) / (28.11 - 6.981);
model.texture = (model.texture - 9.71) / (39.28 - 9.71);
model.perimeter = (model.perimeter - 43.79) / (188.5 - 43.79);
model.area = (model.area - 143.5) / (2501 - 143.5);
//Calculate Prior Probabilities
var priorProbabilityM = Convert.ToDouble(
(from c in dbML.post96
where c.Metric == "Percent Malignant"
select new { c.Value }).Single().Value);
var priorProbabilityB = Convert.ToDouble(
(from c in dbML.post96
where c.Metric == "Percent Benign"
select new { c.Value }).Single().Value);
// Pull Benign Values From the Database
string[,] allkeys = new string[, ] {
{ "Avg Radius", "stdev Radius" },
{ "Avg Texture", "stdev Texture" },
{ "Avg Perimeter", "stdev Perimeter" },
{ "Avg Area", "stdev Area" },
{ "Avg Smoothness", "stdev Smoothness" },
{ "Avg Compactness", "stdev Compactness" },
{ "Avg Concavity", "stdev Concavity" },
{ "Avg Concave Points", "stdev Concave Points" },
{ "Avg Symmetry", "stdev Symmetry" },
{ "Avg Fractal Dimension", "Avg Fractal Dimension" }
};
double[][] numbers = new double[allkeys.Length][];
for (int i = 0; i < 10; i++)
{
double[] statsValues = new double[2];
string key1 = allkeys[i, 0].ToString();
string key2 = allkeys[i, 1].ToString();
statsValues[0] = Convert.ToDouble(
(from c in dbML.post96
where c.Metric == key1
select new { c.Value }).Single().Value);
statsValues[1] = Convert.ToDouble(
(from c in dbML.post96
where c.Metric == key2
select new { c.Value }).Single().Value);
numbers[i] = statsValues;
}
// Pull Malignant Values From the Database
string[,] allkeysM = new string[, ] {
{ "Avg Radius M", "stdev Radius M" },
{ "Avg Texture M", "stdev Texture M" },
{ "Avg Perimeter M", "stdev Perimeter M" },
{ "Avg Area M", "stdev Area M" },
{ "Avg Smoothness M", "stdev Smoothness M" },
{ "Avg Compactness M", "stdev Compactness M" },
{ "Avg Concavity M", "stdev Concavity M" },
{ "Avg Concave Points M", "stdev Concave Points M" },
{ "Avg Symmetry M", "stdev Symmetry M" },
{ "Avg Fractal Dimension M", "Avg Fractal Dimension M" }
};
double[][] numbersM = new double[allkeysM.Length][];
for (int i = 0; i < 10; i++)
{
double[] statsValues = new double[2];
string key1 = allkeysM[i, 0].ToString();
string key2 = allkeysM[i, 1].ToString();
statsValues[0] = Convert.ToDouble(
(from c in dbML.post96
where c.Metric == key1
select new { c.Value }).Single().Value);
statsValues[1] = Convert.ToDouble(
(from c in dbML.post96
where c.Metric == key2
select new { c.Value }).Single().Value);
numbersM[i] = statsValues;
}
//Calculate Likelihoods
var postdiagnosisLikelihoodB =
calculateProbability(model.radius, numbers[0]) *
calculateProbability(model.texture, numbers[1]) *
calculateProbability(model.perimeter, numbers[2]) *
calculateProbability(model.area, numbers[3]) *
calculateProbability(model.smoothness, numbers[4]) *
calculateProbability(model.compactness, numbers[5]) *
calculateProbability(model.concavity, numbers[6]) *
calculateProbability(model.concavePts, numbers[7]) *
calculateProbability(model.symmetry, numbers[8]) *
calculateProbability(model.fractalDimension, numbers[9]);
var postdiagnosisLikelihoodM =
calculateProbability(model.radius, numbersM[0]) *
calculateProbability(model.texture, numbersM[1]) *
calculateProbability(model.perimeter, numbersM[2]) *
calculateProbability(model.area, numbersM[3]) *
calculateProbability(model.smoothness, numbersM[4]) *
calculateProbability(model.compactness, numbersM[5]) *
calculateProbability(model.concavity, numbersM[6]) *
calculateProbability(model.concavePts, numbersM[7]) *
calculateProbability(model.symmetry, numbersM[8]) *
calculateProbability(model.fractalDimension, numbersM[9]);
if ((postdiagnosisLikelihoodB * priorProbabilityB) >= (postdiagnosisLikelihoodM * priorProbabilityM))
{
ViewBag.diagnosis = "Diagnosis: Benign";
ViewBag.probabilityB = "The Likelihood Maximum of a Benign Tumor: " + (postdiagnosisLikelihoodB * priorProbabilityB).ToString();
ViewBag.probabilityM = "The Likelihood Maximun of a Malignant Tumor: " + (postdiagnosisLikelihoodM * priorProbabilityM).ToString();
Highcharts chart = new Highcharts("chart")
.InitChart(new Chart {
DefaultSeriesType = ChartTypes.Bar
})
.SetTitle(new Title {
Text = "Patient Data Comparison"
})
.SetSubtitle(new Subtitle {
Text = "Data for Patient " + model.patientID
})
.SetXAxis(new XAxis
{
Categories = new[] { "Radius", "Texture", "Perimeter", "Area", "Smoothness",
"Compactness",
"Concavity",
"Concave Points",
"Symmetry", "Fractal Dimension" },
Title = new XAxisTitle {
Text = "Cell Attributes"
}
})
.SetYAxis(new YAxis
{
Min = 0,
Title = new YAxisTitle
{
Text = "Values",
Align = AxisTitleAligns.High
}
})
.SetTooltip(new Tooltip {
Formatter = "function() { return ''+ this.series.name +': '+ this.y; }"
})
.SetPlotOptions(new PlotOptions
{
Bar = new PlotOptionsBar
{
DataLabels = new PlotOptionsBarDataLabels {
Enabled = false
}
}
})
.SetCredits(new Credits {
Enabled = false
})
.SetSeries(new[]
{
new Series {
Name = "Average Benign Patient", Data = new Data(new object[] { numbers[0][0], numbers[1][0], numbers[2][0], numbers[3][0], numbers[4][0], numbers[5][0],
numbers[6][0], numbers[7][0], numbers[8][0], numbers[9][0] })
},
new Series {
Name = "Average Malignant Patient", Data = new Data(new object[] { numbersM[0][0], numbersM[1][0], numbersM[2][0], numbersM[3][0], numbersM[4][0], numbersM[5][0],
numbersM[6][0], numbersM[7][0], numbersM[8][0], numbersM[9][0] })
},
new Series {
Name = "Patient " + model.patientID.ToString(), Data = new Data(new object[] { model.radius, model.texture, model.perimeter, model.area,
model.smoothness, model.compactness, model.concavity, model.concavePts, model.symmetry, model.fractalDimension })
}
});
return(View(chart));
}
else
{
ViewBag.diagnosis = "Diagnosis: Malignant";
ViewBag.probabilityB = "The Likelihood Maximum of a Benign Tumor: " + (postdiagnosisLikelihoodB * priorProbabilityB).ToString();
ViewBag.probabilityM = "The Likelihood Maximum of a Malignant Tumor: " + (postdiagnosisLikelihoodM * priorProbabilityM).ToString();
Highcharts chart = new Highcharts("chart")
.InitChart(new Chart {
DefaultSeriesType = ChartTypes.Bar
})
.SetTitle(new Title {
Text = "Patient Data Comparison"
})
.SetSubtitle(new Subtitle {
Text = "Data for Patient " + model.patientID
})
.SetXAxis(new XAxis
{
Categories = new[] { "Radius", "Texture", "Perimeter", "Area", "Smoothness",
"Compactness",
"Concavity",
"Concave Points",
"Symmetry", "Fractal Dimension" },
Title = new XAxisTitle {
Text = "Cell Attributes"
}
})
.SetYAxis(new YAxis
{
Min = 0,
Title = new YAxisTitle
{
Text = "Values",
Align = AxisTitleAligns.High
}
})
.SetTooltip(new Tooltip {
Formatter = "function() { return ''+ this.series.name +': '+ this.y; }"
})
.SetPlotOptions(new PlotOptions
{
Bar = new PlotOptionsBar
{
DataLabels = new PlotOptionsBarDataLabels {
Enabled = false
}
}
})
.SetCredits(new Credits {
Enabled = false
})
.SetSeries(new[]
{
new Series {
Name = "Average Benign Patient", Data = new Data(new object[] { numbers[0][0], numbers[1][0], numbers[2][0], numbers[3][0], numbers[4][0], numbers[5][0],
numbers[6][0], numbers[7][0], numbers[8][0], numbers[9][0] })
},
new Series {
Name = "Average Malignant Patient", Data = new Data(new object[] { numbersM[0][0], numbersM[1][0], numbersM[2][0], numbersM[3][0], numbersM[4][0], numbersM[5][0],
numbersM[6][0], numbersM[7][0], numbersM[8][0], numbersM[9][0] })
},
new Series {
Name = "Patient " + model.patientID.ToString(), Data = new Data(new object[] { model.radius, model.texture, model.perimeter, model.area,
model.smoothness, model.compactness, model.concavity, model.concavePts, model.symmetry, model.fractalDimension })
}
});
return(View(chart));
}
}
