REVISTA DYNA NEW TECHNOLOGIES

Key words:

Predicción, redes neuronales artificiales, ciencia de datos, trastornos físicos, trastornos mentales, Prediction, artificial neural networks, data science, physical disorders, mental disorders

Article type:

ARTICULO DE INVESTIGACION / RESEARCH ARTICLE

Section:

RESEARCH ARTICLES

ABSTRACT:
The aim of this work was to build four models for the prediction of Noncommunicable Diseases (NCD) to support early diagnosis by applying deep Artificial Neural Networks (ANN) and clustering; through comparative analysis, the best paradigm was chosen. Following the methodology for data science, the objectives, project requirements and the quality of the data were analyzed, cleaning and normalization was carried out, the identification of the optimal number of groups by means of the silhouette method. Each model was trained with 70% of records and validated with the remaining 30% using the confusion matrix and F1. The comparative analysis showed the best performance of the multilayer perceptron with deep learning (over a network with clustering radial basis functions), its accuracy, precision, sensitivity, specificity and F1 were 99%, 97%, 100%, 97% and 98% respectively for lung cancer, 99%, 98%, 100%, 100% and 98% for breast cancer, Alzheimer with 98.6%, 100%, 96%, 100% and 97%, and depression with 91% , 87%, 93%, 88% and 88%, these models constitute an automated solution to strengthen medical diagnosis, they are considered a strategic support in the prevention of NCD, will contribute to improving the prognosis and quality of life of vulnerable people.
Keywords: Prediction, artificial neural networks, data science, physical disorders, mental disorders