Enhancing Prediction Accuracy of Chronic Kidney Disease using Neural Networks
Chronic kidney disease also known, as CKD is a medical condition, which indicates deteriorating kidney functionality and makes the person feel sick, weak and decreases the ability to stay healthy. Prolonged suffering of kidney disease increases the impurities to high level in the blood and could develop complications like high blood pressure, anaemia (low blood count), weak bones, poor nutritional health and nerve damage. CKD also increases the risk of having heart failure and blood vessel disease. These problems may happen slowly over a long period of time. This paper aims at predicting the early detection of chronic kidney disease also known as chronic renal disease for diabetic patients with the help of machine learning methods. The Neural Networks is a model in the data mining space, which is mainly used to understand the relationship among data through its interconnection. In this case, simulated neurons accept inputs and apply weighting coefficients, further feed its output to other neurons. This process will continue throughout the network and eventually leads to high quality output. The neural networks are trained to deliver the desired result by an iterative process where the weights applied to each input at each neuron are adjusted to optimize the desired output. This method is often compared with decision trees, since both methods help to model data and has nonlinear relationships between variables. The neural networks concept seems difficult compared to decision trees, whereas it will generate higher quality output than decision trees.
Al-Hyari, A. Y., Al-Taee, A. M., & Al-Taee, M. A. (2013, December). Clinical decision support system for diagnosis and management of chronic renal failure. In Applied Electrical Engineering and Computing Technologies (AEECT), 2013 IEEE Jordan Conference on (pp. 1-6). IEEE.
Al-Hyari, A. Y., Al-Taee, A. M., & Al-Taee, M. A. (2014). Diagnosis and classification of chronic renal failure utilising intelligent data mining classifiers. International Journal of Information Technology and Web Engineering (IJITWE), 9(4), 1-12.
Aljumah, A. A., Ahamad, M. G., & Siddiqui, M. K. (2011). Predictive analysis on hypertension treatment using data mining approach in Saudi Arabia. Intelligent Information Management, 3(06), 252.
Bala, S., & Kumar, K. (2014). A literature review on kidney disease prediction using data mining classification technique. International Journal of Computer Science and Mobile Computing, 3(7), 960-967.
Bellos, C., Papadopoulos, A., Rosso, R., & Fotiadis, D. I. (2011, August). Heterogeneous data fusion and intelligent techniques embedded in a mobile application for real-time chronic disease management. In Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE (pp. 8303-8306). IEEE.
Bhatla, N., & Jyoti, K. (2012). An analysis of heart disease prediction using different data mining techniques. International Journal of Engineering, 1(8), 1-4.
Breit, M., & Weinberger, K. M. (2016). Metabolic biomarkers for chronic kidney disease. Archives of biochemistry and biophysics, 589, 62-80.
Chawla, N. V., & Davis, D. A. (2013). Bringing big data to personalize
Dangare, C. S., & Apte, S. S. (2012). Improved study of heart disease prediction system using data mining classification techniques. International Journal of Computer Applications, 47(10), 44-48.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.