In recent years, the clinical decision support system (DSS) has emerged as an important area in medical sciences to assist clinicians in medical diagnosis. Health records classification is based on learning from various health datasets to improve the better quality of DSS in health care. The main objective of this investigation is to establish a system for the successful classification of health data. Orthogonal Local Preservation Projection (OLPP) has been used to obtain promising outcomes in medical data classification. This is a high-dimensional data input package. A feature-reduction tool is then used to reduce the functionality space without compromising the calculation accuracy. The Artificial Neural Network shall be used as a classifier. We used an optimization algorithm to boost efficiency. The "artificial bee colony algorithm" is a bio-based optimization algorithm a neural network uses. The medical datasets represent the average improvement in the proposed system classification quality with the current form, around 15%.