A Hypothyroidism Prediction using Supervised Algorithm
Shalini L1, Muhammad Rukunuddin Ghalib2
1Shalini L School of Computer Science and Engineering, Vellore Institute of Technology(VIT) Deemed to be University, Vellore, Tamil Nadu, India.
2Dr. Muhammad Rukunuddin Ghalib ,School of Computer Science and Engineering, Vellore Institute of Technology(VIT) Deemed to be University, Vellore, Tamil Nadu, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 7285-7288 | Volume-9 Issue-1, October 2019 | Retrieval Number: F9322088619/2019©BEIESP | DOI: 10.35940/ijeat.F9322.088619
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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: Thyroid issue are pervasive and their appearances are dictated by the dietary iodine accessibility. The most well-known reason for thyroid issue worldwide is iodine deficiency, inducing growth in goiter and hypothyroidism. In many regions, the people with thyroid issue have iodine deficiency which leads to poor have immune system. The greater parts of the populace are undiscovered or misdiagnosed. Ladies are multiple times bound to contract thyroid issues than men and almost 50% everything being equal and a fourth of all men will kick the bucket with proof of an induced thyroid. The side effects of this sickness regularly shift from individual to individual and are non-explicit, so a right finding can without much of a stretch be missed or misdiagnosed for immaterial issues. In light of the trial directed it demonstrates that Rand forest and Support Vector Machine gives result closest in anticipating the illnesses. This paper points in diagnosing the Hypothyroidism utilizing different classification. The precision of the every classifier helps in distinguishing the sicknesses. A modified Support Vector Machine (SVM) that uses Convex hull to compute the support vectors is proposed. The proposed SVM is evaluated on the UCI Thyroid dataset.
Keywords: Classification Algorithm, Hypothyroidism, Random forest, Support Vector Machine.