An Effective Patient Treatment Plan Recommendation with Predicted Treatment Time Using Hadoop

Abstract

satisfactory patient queuing system to limit patient waits and patient congestion is a specific problem confronted by most of the hospitals. Unavoidable and irritating waits for prolonged intervals result in generous human efforts, misuse of time and also raise the dissatisfaction persisted by patients. For each individual in the line, the absolute treatment time of overall patients leading him endures the time that fellow should stay. It could be helpful and ideal if patients could have the knowledge about the treatment design and learn the foreseen time for holding up. Thus, a Patient Treatment Time Prediction (PTTP) method is used to estimate the delay time of treatment activities for an individual. We make use of patient factual records of different clinical centers to get a person’s treatment time consumption procedure for each treatment duty. Over the vast extent, and practical data set, the treatment time for an individual in the line of each operation is anticipated. Build upon the forecast delay time, a Queuing Recommendation (QR) process is produced. Queuing Recommendation framework computes and predicts the proficiency and helpful treatment schedule prescribed for the patient. To accomplish this, patient records are collected from different clinical centers and stored in the Hadoop environment. Enhanced Random Forest (RF) technique is used to educate the treatment time consumption. Thus, every individual in line can be suggested completing their treatment activities in the easiest way and with the appropriate time.