Supervised Learning Algorithms for Detection of Brain Tumour
Pranitha Bhuta1, Konda HimaKireeti2, Nazma Mohammad3
1Pranitha Bhuta, Department of Computer Science, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India.
2Konda Himakireeti, Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India.
3Nazma Mohammad, Department of Computer Science, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 1099-1102 | Volume-8 Issue-8, June 2019 | Retrieval Number: H6588068819/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Over the past few years in the field of medicine, data mining is being used for the prediction of diseases. Data mining is the technique of extracting significant data from massive warehouses or repositories or other datasets. Brain tumour is inherently serious and fatal due to its behaviour in the confined space of the intracranial cavity. Several sophisticated methods are being used in detecting the brain tumour such as Biopsy, Angiogram, Magnetic Resonance Imaging and Spinal Tap. The treatment can be predicted by proper diagnosis in early stages. World Health Organization reclassified all types of brain tumour officially. There are 120 types of Brain tumours, having similar symptoms and hence the treatment cannot be predicted easily. Studies have determined that most of people with brain tumours have died as a result of inaccurate detection. To overcome this, in this paper we have proposed an effective and precise algorithm that predicts the type of brain tumour. Algorithms such as Decision Tree and Naïve Bayes’ classification are chosen. The focus of this paper is how these algorithms would classifying the types of brain tumours with ease and accuracy.
Keyword: Entropy, Gain, Decision tree, Symptoms, Classifier.
Scope of the Article: Web-Based Learning: Innovation and Challenges.