Name
Abstract | ||
|---|---|---|
Machine learning has earned a remarkable position in healthcare sector because of its capability to enhance the disease prediction in healthcare sector. Artificial intelligence and Machine learning techniques are being used in healthcare sector. Nowadays, one of the world's crucial health related problem is kidney disease. It is increasing day by day because of not maintaining proper food habits, drinking less amount of water and lack of health consciousness. So we need some technique that will continuously monitor health condition effectively. Here, we have proposed an approach for real time kidney disease prediction, monitoring and application (KDPMA). Our aim is to find an optimized and efficient machine learning (ML) technique that can effectively recognize and predict the condition of chronic kidney disease. In this work, we used ten most popular machine learning technique to predict kidney disease. In this process, the data has been divided into two sections. In one section train dataset got trained and another section got evaluated by test dataset. The analysis results show that Decision Tree Classifier and Gaussian Naive Bayes achieved highest performance than the other classifiers, obtaining the accuracy score of 100% and 1 recall(Sensitivity) score. Now we are developing mobile application based on the best output results classifier technique to predict Kidney Disease from patient report. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icccnt45670.2019.8944799 | 2019 10TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND NETWORKING TECHNOLOGIES (ICCCNT) |
Keywords | Field | DocType |
Machine Learning, Health Care, Kidney disease | Health care,Disease,Naive Bayes classifier,Computer science,Kidney disease,Artificial intelligence,Classifier (linguistics),Recall,Decision tree learning,Machine learning,Patient report | Conference |
ISSN | Citations | PageRank |
2162-7665 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| AKM Shahariar Azad Rabby | 1 | 0 | 1.01 |
| Rezwana Mamata | 2 | 0 | 0.34 |
| Monira Akter Laboni | 3 | 0 | 0.34 |
| Ohidujjaman | 4 | 0 | 1.01 |
| Sheikh Abujar | 5 | 0 | 2.03 |