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Neural Network and Support Vector Machine for the Prediction of Chronic…

- - - - ability to learn how to perform its functions once
      - ability to generalize and produce a reasonable solution
    - - Input layer
        
        data/features are received, normalization techniques (output better precision because input limitation to a certain range)
      - Hidden (intermediate/invisible) layer
      - Output layer
        
        single-layer feedforward (one direction)
        
        single input single output (neural layer without hidden)
        
        pattern classification and linear filtering
        
        mesh
        
        recurrent
        
        multilayer feedforward
    - - Seed=0
      - Hidden layers=a
      - Learning rate=0.3
  - - - Polynomial kernel
      - Linear Kernel
      - Gaussian kernel
- - - - Age, Blood pressure, Blood glucose random, Blood Urea, Scrum Creatinine, Sodium, Potassium, Hemoglobin, Packed Cell Volume, White Blood Cell Count, Red Blood Cell Count
        
        Mean, Median, Standard Deviation, Minimum, Maximum
    - - Specific Gravity, Albumin, Sugar, Red Blood Cells, Pus Cell, Pus Cell Clumps, Bacteria, Hypertention, Diabetes Mellitus, Coronary Artery Disease, Appetite, Pedal Edema, Anemia
        
        Preprocessing, The mean of numerical distribution replaced the missing value