Early detection of any disease is very important since it aids curable with a few of effort. A lot of people fail to detection their disease before it be chronic. This causes an increase in mortality about the world. One of these diseases is breast cancer that can be cured when identified in the early stages before it spreads throughout the body. Develop techniques that can aide physicians to get accurate diagnosis is significantly important in early detection for this disease. The goal is design a hybrid approach (class association rules and deep neural network). In this paper, we design efficient methodology for classifying breast cancer using hybrid approach techniques. Where used a CARs to discover all the interesting relationship in a large database, while the DNN is used for classification purpose. In this study, use Wisconsin Breast cancer dataset from UCI machine learning repository to evaluate the performance of the proposed system. The experiment show that proposed system achieves good results, with high accuracy of 100% and less mean square error rate 0.0002.
Chronic kidney disease (CKD) develops gradually, usually after months or years when the kidneys lose function. In general, it may not be detected before it loses 25% of its functionality. Patients may begin to not recognize kidney failure because kidney failure may not give any symptoms at first. Treatment for kidney failure aims to control the causes and slow the progression of kidney failure. If the treatments are insufficient, the patient is in the end stage of kidney failure and the last treatment is dialysis or a kidney transplant. at this time. Therefore, it is necessary to make an early diagnosis to avoid reaching the stage of kidney failure. We conclude in this paper that the Naive Bayes algorithm is one of the best algorithms for diagnosing diseases with high accuracy of 99.24% and time of 0.003 seconds approximately because it is suitable for this kind of dataset.
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