Multi Disease Classification System Based on Symptoms using The Blended Approach

In today’s world, everyone is preoccupied with work and other activities, leav- ing little time to visit doctors about illnesses that may appear be minor at first but develop into life-threatening conditions as passes. As a result, the proposed model accesses public repository maintains numerous symp- toms their possible diseases matrix for early disease prediction prevention. Symptoms are received from user fed embed- ded blending algorithm estimate type of disease. The patient’s records collected several hospitals resulting massive volume data, which results in inefficient using machine learning approaches. Since combined approach training mechanism, it can reduce number accessing every step. Tra- ditional approaches like bagging boosting construct more deci- sion trees because vast amount data. This utilization resources sometimes CPU enters saturation state. system solves this problem by optimized parameters tree construction reduces memory resource utilizations.