MACHINE LEARNING FOR DIABETES AND BLOOD GLUCOSE PREDICTION
Uitgelicht
|
20,00 |
Naar shop
|
|
41,42 |
Naar shop
|
|
41,42 |
Naar shop
|
| VERGELIJK ALLE AANBIEDERS (3) | ||
Beschrijving
Bol
This book describes various ML techniques and DL algorithms used to apply in diabetes prediction.such as NB, RF, SVM, DT, J48, Sequential Minimal Optimization (SMO), ANN (Artificial Neural Network), Multi-LayerPerceptron (MLP), Recurrent Neural Network (RNN), CNN and LSTM. Althoughdeep research in diabetes mellitus has provided strong proficiency to classify data,there is still much to be discovered by considering the patient's diet, medication,activity, and biological, environmental, behavioral, and decision factors. TraditionalML models are easy to interpret but achieve less predictive accuracy while ensemblemodels provide higher accuracy. Existing studies proved that DL models performedcomparatively better on complex datasets than the ML models.
Beschrijving
(1)
This book describes various ML techniques and DL algorithms used to apply in diabetes prediction.such as NB, RF, SVM, DT, J48, Sequential Minimal Optimization (SMO), ANN (Artificial Neural Network), Multi-LayerPerceptron (MLP), Recurrent Neural Network (RNN), CNN and LSTM. Althoughdeep research in diabetes mellitus has provided strong proficiency to classify data,there is still much to be discovered by considering the patient's diet, medication,activity, and biological, environmental, behavioral, and decision factors. TraditionalML models are easy to interpret but achieve less predictive accuracy while ensemblemodels provide higher accuracy. Existing studies proved that DL models performedcomparatively better on complex datasets than the ML models.
Productspecificaties
Prijshistorie
* Prijshistorie bevat geen data van Amazon, Amazon Marketplace.
Prijzen voor het laatst bijgewerkt op:
Gerelateerde resultaten
