The optimal time slot selection and feature selection for the prediction of drugs for diseases

Data mining techniques have been applied to analyze, predict and diagnose diseases. The prediction of disease becomes meaningless when there is no proper recommendation of a drug to the patient. A drug recommendation method called Artificial Neural Network (ANN) with side effect constraints was proposed to recommend drug names for multiple diseases such as Chronic Kidney Disease (CKD), diabetic and heart disease based on the interaction between drug and disease and their side effects. In this drug recommendation method, multiple attributes of drugs and patients were collected from different sources and the hidden relationship between the attributes was predicted by using a Hidden Markov Model (HMM). In addition to this, statistical features were calculated and added as additional features. The collected and calculated features were used in ANN with side effect constraint classifier which predicted drug name for multiple diseases with the consideration of side effects. However, there is a high dimensionality problem in the recommended method due to more number of features. Moreover, it leads to more computational and space complexity in the ANN classifier. In this paper, an efficient Krill Herd (KH) algorithm for optimization is introduced to solve the above-mentioned problems in the drug recommendation method. According to the herding behavior of the likeness of the krill individuals, KH selects the optimal features. The multiple attributes of drugs and patients are collected in a different time slots. The KH algorithm is also used to select the optimal time slot. Then, the optimal time slot and features are given as input to ANN which predicts drug names for multiple diseases with high accuracy and low computational complexity.