def calibration_belt():
args = [
"-x",
"probGiViTI_2017_Complessiva",
"-y",
"hospOutcomeLatest_RIC10",
"-devel",
"internal",
"-max_deg",
"4",
"-confLevels",
"0.80, 0.95",
"-thres",
"0.95",
"-num_points",
"200",
"-pathology",
"dementia",
"-dataset",
"cb_data",
"-filter",
"",
"-formula",
"",
]
result = get_algorithm_result(CalibrationBelt, args)
result = result["result"][1]["data"]
return render_template(
"highchart_layout.html", title="Calibration Belt", data=result,
)
def kaplan_meier_survival():
args = [
"-x",
"apoe4",
"-y",
"alzheimerbroadcategory",
"-pathology",
"dementia",
"-dataset",
"alzheimer_fake_cohort",
"-filter",
"""
{
"condition":"OR",
"rules":[
{
"id":"alzheimerbroadcategory",
"field":"alzheimerbroadcategory",
"type":"string",
"input":"select",
"operator":"equal",
"value":"AD"
},
{
"id":"alzheimerbroadcategory",
"field":"alzheimerbroadcategory",
"type":"string",
"input":"select",
"operator":"equal",
"value":"MCI"
}
],
"valid":true
}
""",
"-outcome_pos",
"AD",
"-outcome_neg",
"MCI",
"-total_duration",
"1100",
]
result = get_algorithm_result(KaplanMeier, args)
result = result["result"][1]["data"]
return render_template("highchart_layout.html", title="Kaplan Meier", data=result,)
def logistic_confmat():
args = [
"-x",
"lefthippocampus",
"-y",
"alzheimerbroadcategory",
"-pathology",
"dementia",
"-dataset",
"adni",
"-filter",
"""
{
"condition": "OR",
"rules": [
{
"id": "alzheimerbroadcategory",
"field": "alzheimerbroadcategory",
"type": "string",
"input": "text",
"operator": "equal",
"value": "AD"
},
{
"id": "alzheimerbroadcategory",
"field": "alzheimerbroadcategory",
"type": "string",
"input": "text",
"operator": "equal",
"value": "CN"
}
],
"valid": true
}
""",
"-formula",
"",
]
result = get_algorithm_result(LogisticRegression, args)
result = result["result"][3]["data"]
return render_template(
"highchart_layout.html",
title="Logistic Regression Confusion Matrix",
data=result,
)
def pca_scree_eigenvalues():
pca_args = [
"-y",
"subjectage,rightventraldc,rightaccumbensarea, gender",
"-pathology",
"dementia, leftaccumbensarea",
"-dataset",
"adni",
"-filter",
"",
"-formula",
"",
"-coding",
"Treatment",
]
result = get_algorithm_result(PCA, pca_args)
result = result["result"][3]["data"]
return render_template("highchart_layout.html", title="PCA scree plot", data=result)
def anova_errorbars():
anova_args = [
"-y",
"lefthippocampus",
"-x",
"alzheimerbroadcategory",
"-pathology",
"dementia",
"-dataset",
"adni",
"-filter",
"",
]
result = get_algorithm_result(Anova, anova_args)
result = result["result"][3]["data"]
return render_template(
"highchart_layout.html", title="Anova Mean Plot", data=result
)
def pearson_heatmap():
args = [
"-x",
"",
"-y",
"leftputamen, righthippocampus, subjectage,rightventraldc,rightaccumbensarea, "
"rightioginferioroccipitalgyrus,rightmfcmedialfrontalcortex, lefthippocampus,"
"rightppplanumpolare",
"-pathology",
"dementia, leftaccumbensarea",
"-dataset",
"adni",
"-filter",
"",
"-formula",
"",
"-coding",
"",
]
result = get_algorithm_result(Pearson, args)
result = result["result"][2]["data"]
return render_template(
"highchart_layout.html", title="Pearson Correlation Heatmap", data=result
)
def naive_bayes_roc():
result = get_algorithm_result(NaiveBayes, nb_args)
result = result["result"][5]["data"]
return render_template("highchart_layout.html", title="NaiveBayes ROC", data=result)
def naive_bayes_confusion_matrix():
result = get_algorithm_result(NaiveBayes, nb_args)
result = result["result"][4]["data"]
return render_template(
"highchart_layout.html", title="NaiveBayes Confusion Martix", data=result
)
