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mcp-regularized backpropagation neural network  (SoftMax Inc)

 
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    SoftMax Inc mcp-regularized backpropagation neural network
    Mcp Regularized Backpropagation Neural Network, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/mcp-regularized+backpropagation+neural+network/mcp+regularized+backpropagation+neural+network/pm37301891-125-51-54
    Average 90 stars, based on 1 article reviews
    mcp-regularized backpropagation neural network - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    Significance Assay:

    Article Title: Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study.
    Article Snippet: Algorithm Selected variables Normalized importance value % P-value Total number of medications at baseline 14 < 0.001 Diabetes mellitus 7 < 0.001 Renin-angiotensin system inhibitors two or more years before dementia diagnosis 7 0.449 Hypertension 5 < 0.001 Acute Kidney Injury 3 0.003 Cancer 2 0.002 Liver failure 2 0.062 MCP-regularized Backpropagation Neural Network (Softmax activation function, lambda tuning’s parameter:0.0020) Age at dementia diagnosis 100 < 0.001 BMI 89 < 0.001 MMSE score 75 < 0.001 Diuretics 51 < 0.001 Time from referral to initiation of work-up 44 0.367 Time from initiation of work-up to diagnosis 42 < 0.001 Atorvastatin 34 < 0.001 Basic dementia diagnostic work-up 30 < 0.001 Sex 25 < 0.001 Charlson comorbidity index 21 < 0.001 Dementia medications 20 < 0.001 Municipality 18 0.626 Total number of medications at baseline 16 < 0.001 Heart failure 16 0.012 Physiotherapist assessment 14 < 0.001 Care unit (primary care vs specialist care) 14 < 0.001 Cholinesterase inhibitors 13 < 0.001 Atrial fibrillation 13 < 0.001 Rosuvastatin 12 < 0.001 Place of residency 11 0.001 Alcohol related diagnosis 10 < 0.001 Renin-angiotensin system inhibitors 9 0.635 Renin-angiotensin system inhibitors two or more years before dementia diagnosis 9 0.449 Dementia type 8 < 0.001 Diabetes mellitus 8 < 0.001 Blood tests 7 0.902 Losartan 7 0.001 Statins 7 < 0.001 Antidepressants 4 0.135 Cardiovascular medication at diagnosis 4 < 0.001 Renal disease 4 < 0.001 Cancer 3 0.002 Irbesartan 3 0.78 Liver failure 3 0.062 Captopril 2 0.038 Valsartan 2 0.096 Calcium channel blockers 2 0.367 Heart failure 2 0.359 Fluvastatin 1 0.421 Anemia 1 < 0.001 Table 2.

    Medications:

    Article Title: Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study.
    Article Snippet: Algorithm Selected variables Normalized importance value % P-value Total number of medications at baseline 14 < 0.001 Diabetes mellitus 7 < 0.001 Renin-angiotensin system inhibitors two or more years before dementia diagnosis 7 0.449 Hypertension 5 < 0.001 Acute Kidney Injury 3 0.003 Cancer 2 0.002 Liver failure 2 0.062 MCP-regularized Backpropagation Neural Network (Softmax activation function, lambda tuning’s parameter:0.0020) Age at dementia diagnosis 100 < 0.001 BMI 89 < 0.001 MMSE score 75 < 0.001 Diuretics 51 < 0.001 Time from referral to initiation of work-up 44 0.367 Time from initiation of work-up to diagnosis 42 < 0.001 Atorvastatin 34 < 0.001 Basic dementia diagnostic work-up 30 < 0.001 Sex 25 < 0.001 Charlson comorbidity index 21 < 0.001 Dementia medications 20 < 0.001 Municipality 18 0.626 Total number of medications at baseline 16 < 0.001 Heart failure 16 0.012 Physiotherapist assessment 14 < 0.001 Care unit (primary care vs specialist care) 14 < 0.001 Cholinesterase inhibitors 13 < 0.001 Atrial fibrillation 13 < 0.001 Rosuvastatin 12 < 0.001 Place of residency 11 0.001 Alcohol related diagnosis 10 < 0.001 Renin-angiotensin system inhibitors 9 0.635 Renin-angiotensin system inhibitors two or more years before dementia diagnosis 9 0.449 Dementia type 8 < 0.001 Diabetes mellitus 8 < 0.001 Blood tests 7 0.902 Losartan 7 0.001 Statins 7 < 0.001 Antidepressants 4 0.135 Cardiovascular medication at diagnosis 4 < 0.001 Renal disease 4 < 0.001 Cancer 3 0.002 Irbesartan 3 0.78 Liver failure 3 0.062 Captopril 2 0.038 Valsartan 2 0.096 Calcium channel blockers 2 0.367 Heart failure 2 0.359 Fluvastatin 1 0.421 Anemia 1 < 0.001 Table 2.

    Activation Assay:

    Article Title: Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study.
    Article Snippet: Algorithm Selected variables Normalized importance value % P-value Total number of medications at baseline 14 < 0.001 Diabetes mellitus 7 < 0.001 Renin-angiotensin system inhibitors two or more years before dementia diagnosis 7 0.449 Hypertension 5 < 0.001 Acute Kidney Injury 3 0.003 Cancer 2 0.002 Liver failure 2 0.062 MCP-regularized Backpropagation Neural Network (Softmax activation function, lambda tuning’s parameter:0.0020) Age at dementia diagnosis 100 < 0.001 BMI 89 < 0.001 MMSE score 75 < 0.001 Diuretics 51 < 0.001 Time from referral to initiation of work-up 44 0.367 Time from initiation of work-up to diagnosis 42 < 0.001 Atorvastatin 34 < 0.001 Basic dementia diagnostic work-up 30 < 0.001 Sex 25 < 0.001 Charlson comorbidity index 21 < 0.001 Dementia medications 20 < 0.001 Municipality 18 0.626 Total number of medications at baseline 16 < 0.001 Heart failure 16 0.012 Physiotherapist assessment 14 < 0.001 Care unit (primary care vs specialist care) 14 < 0.001 Cholinesterase inhibitors 13 < 0.001 Atrial fibrillation 13 < 0.001 Rosuvastatin 12 < 0.001 Place of residency 11 0.001 Alcohol related diagnosis 10 < 0.001 Renin-angiotensin system inhibitors 9 0.635 Renin-angiotensin system inhibitors two or more years before dementia diagnosis 9 0.449 Dementia type 8 < 0.001 Diabetes mellitus 8 < 0.001 Blood tests 7 0.902 Losartan 7 0.001 Statins 7 < 0.001 Antidepressants 4 0.135 Cardiovascular medication at diagnosis 4 < 0.001 Renal disease 4 < 0.001 Cancer 3 0.002 Irbesartan 3 0.78 Liver failure 3 0.062 Captopril 2 0.038 Valsartan 2 0.096 Calcium channel blockers 2 0.367 Heart failure 2 0.359 Fluvastatin 1 0.421 Anemia 1 < 0.001 Table 2.

    Diagnostic Assay:

    Article Title: Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study.
    Article Snippet: Algorithm Selected variables Normalized importance value % P-value Total number of medications at baseline 14 < 0.001 Diabetes mellitus 7 < 0.001 Renin-angiotensin system inhibitors two or more years before dementia diagnosis 7 0.449 Hypertension 5 < 0.001 Acute Kidney Injury 3 0.003 Cancer 2 0.002 Liver failure 2 0.062 MCP-regularized Backpropagation Neural Network (Softmax activation function, lambda tuning’s parameter:0.0020) Age at dementia diagnosis 100 < 0.001 BMI 89 < 0.001 MMSE score 75 < 0.001 Diuretics 51 < 0.001 Time from referral to initiation of work-up 44 0.367 Time from initiation of work-up to diagnosis 42 < 0.001 Atorvastatin 34 < 0.001 Basic dementia diagnostic work-up 30 < 0.001 Sex 25 < 0.001 Charlson comorbidity index 21 < 0.001 Dementia medications 20 < 0.001 Municipality 18 0.626 Total number of medications at baseline 16 < 0.001 Heart failure 16 0.012 Physiotherapist assessment 14 < 0.001 Care unit (primary care vs specialist care) 14 < 0.001 Cholinesterase inhibitors 13 < 0.001 Atrial fibrillation 13 < 0.001 Rosuvastatin 12 < 0.001 Place of residency 11 0.001 Alcohol related diagnosis 10 < 0.001 Renin-angiotensin system inhibitors 9 0.635 Renin-angiotensin system inhibitors two or more years before dementia diagnosis 9 0.449 Dementia type 8 < 0.001 Diabetes mellitus 8 < 0.001 Blood tests 7 0.902 Losartan 7 0.001 Statins 7 < 0.001 Antidepressants 4 0.135 Cardiovascular medication at diagnosis 4 < 0.001 Renal disease 4 < 0.001 Cancer 3 0.002 Irbesartan 3 0.78 Liver failure 3 0.062 Captopril 2 0.038 Valsartan 2 0.096 Calcium channel blockers 2 0.367 Heart failure 2 0.359 Fluvastatin 1 0.421 Anemia 1 < 0.001 Table 2.



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    Selected variables to predict mortality risk based on the training set (N = 18,682) using Elastic-net logistic regression (“glmnet” R package), SCAD- support vector machine (“penalizedSVM” R package), and <t> MCP- neural network </t> algorithm with repeated tenfold cross-validation in the training set (“neuralnet” and “ncvreg” R packages).
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    Image Search Results


    Selected variables to predict mortality risk based on the training set (N = 18,682) using Elastic-net logistic regression (“glmnet” R package), SCAD- support vector machine (“penalizedSVM” R package), and MCP- neural network algorithm with repeated tenfold cross-validation in the training set (“neuralnet” and “ncvreg” R packages).

    Journal: Scientific Reports

    Article Title: Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study

    doi: 10.1038/s41598-023-36362-3

    Figure Lengend Snippet: Selected variables to predict mortality risk based on the training set (N = 18,682) using Elastic-net logistic regression (“glmnet” R package), SCAD- support vector machine (“penalizedSVM” R package), and MCP- neural network algorithm with repeated tenfold cross-validation in the training set (“neuralnet” and “ncvreg” R packages).

    Article Snippet: MCP-regularized Backpropagation Neural Network (Softmax activation function, lambda tuning’s parameter:0.0020) , Age at dementia diagnosis , 100 , < 0.001.

    Techniques: Plasmid Preparation, Biomarker Discovery, Diagnostic Assay, Medications, Activation Assay