ANN for Diabetic Prediction by Using Chaotic Based Sine Cosine Algorithm

The use of AI is becoming increasingly widespread in medical diagnosis. Recently, many decision-making systems have used the Artificial Neural Networks (ANN) model to train the ANN’s weight and biases to get the lowest error function and highest accuracy. In this concern meta-heuristic based optimization technique play an important role. Already various optimization techniques have been applied to train an ANN’s weight and bias. But due to improper balancing between exploration and exploitation they fail to give the global optima. To overcome this issues, this study used a new stochastic-based optimization algorithm the Sine Cosine Algorithm (SCA). The mathematical formulation of SCA is based on trigonometric functions, sine and cosine. However, sometimes slow convergence is the main disadvantage of the basic SCA algorithm. This paper proposes a modified SCA optimization technique called Chaotic SCA(CSCA) to train the control parameters like weights and biases of a single-layer ANN by integrating chaotic into SCA to expedite the convergence speed. The performance of the above algorithm is examined and verified using The Pima Indian data set. The experiment revealed the outperformance of CSCA than the other algorithms.