Applying Soft Computing to Clinical Decision Support

Applying Soft Computing to Clinical Decision Support

José Machado (Universidade do Minho, Portugal), Lucas Oliveira (Universidade do Minho, Portugal), Luís Barreiro (Universidade do Minho, Portugal), Serafim Pinto (Universidade do Minho, Portugal) and Ana Coimbra (Universidade do Minho, Portugal)
DOI: 10.4018/978-1-4666-9882-6.ch013
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Abstract

This article aims to explain the construction process of the learing systems based on Artificial Neural Networks and Genetic Algorithms. These systems were implemented using R and Python programming languages, in order to compare results and achieve the best solution and it was used Diabetes and Parkinson datasets with the purpose of identifying the carriers of these diseases.
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Datasets

For this work it was select two datasets of public domain, available for download in https://archive.ics.uci.edu/ml/datasets.html. The first is about the Diabetes disease, more particularly in female individuals with at least 21 years old and Pima Indian descent. It is composed by 9 attributes and 768 instances, to know:

  • 1.

    Number of times pregnant

  • 2.

    Plasma glucose concentration a 2 hours in an oral glucose tolerance test

  • 3.

    Diastolic blood pressure (mm Hg)

  • 4.

    Triceps skin fold thickness (mm)

  • 5.

    2-Hour serum insulin (mu U/ml)

  • 6.

    Body mass index (weight in kg/(height in m))2)

  • 7.

    Diabetes pedigree function

  • 8.

    Age (years)

  • 9.

    Class variable (0 or 1)

The second dataset analyzes various biomedical measures corresponding to Parkinson disease or not. The main goal of the information is discriminate the people that are Parkinson patients. The information is divided in 23 attributes and 197 instances, to know:

  • 1.

    Name - ASCII subject name and recording number

  • 2.

    MDVP:Fo(Hz) - Average vocal fundamental frequency

  • 3.

    MDVP:Fhi(Hz) - Maximum vocal fundamental frequency

  • 4.

    MDVP:Flo(Hz) - Minimum vocal fundamental frequency

  • 5.

    MDVP:Jitter(%),MDVP:Jitter(Abs),MDVP:RAP,MDVP:PPQ,Jitter:DDP - Several measures of variation in fundamental frequency

  • 6.

    MDVP:Shimmer,MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:APQ,Shimmer:DDA - Several measures of variation in amplitude

  • 7.

    NHR, HNR - Two measures of ratio of noise to tonal components in the voice

  • 8.

    Status - Health status of the subject (one) - Parkinson's, (zero) - healthy

  • 9.

    RPDE, D2 - Two nonlinear dynamical complexity measures

  • 10.

    DFA - Signal fractal scaling exponent

  • 11.

    spread1, spread2, PPE - Three nonlinear measures of fundamental frequency variation

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