Symptoms Based Multiple Disease Prediction Model using Machine Learning Approach
Talasila Bhanuteja1, Kilaru Venkata Narendra Kumar2, Kolli Sai Poornachand3, Chennupati Ashish4, Poonati Anudeep5
1Talasila Bhanuteja*, School of Computer Science And Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
2Kilaru Venkata Narendra Kumar, School of Computer Science And Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
3Kolli Sai Poornachand, School of Computer Science And Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
4Chennupati Ashish, School of Computer Science And Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
5Poonati Anudeep, School of Computer Science And Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Manuscript received on June 20, 2021. | Revised Manuscript received on June 30, 2021. | Manuscript published on July 30, 2021. | PP: 67-72 | Volume-10, Issue-9, July 2021 | Retrieval Number: 100.1/ijitee.I93640710921 | DOI: 10.35940/ijitee.I9364.0710921
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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: The turn of events and misuse of a few noticeable Data mining strategies in various genuine application regions (for example Trade, Medical management and Natural science) has induced the usage of such methods in Machine Learning (ML) constrains, to distinct helpful snippets of information of the predefined information in medical services networks, biomedical fields and so forth The exact examination of clinical data set advantages in early illness expectation, patient consideration and local area administrations. The methodology of Machine Learning (ML) has been effectively utilized in grouped technologies including Disease forecast. The objective of generating classifier framework utilizing Machine Learning (ML) models is to massively assist with addressing the well-being related issues by helping the doctors to foresee and analyze illnesses at a beginning phase. Sample information of 4920 patient’s records determined to have 41 illnesses was chosen for examination. A reliant variable was made out of 41 sicknesses. 95 of 132 autonomous variables (symptoms) firmly identified with infections were chosen and advanced. This examination work completed shows the illness expectation framework created utilizing Machine learning calculations like Random Forest, Decision Tree Classifier and Light GBM. The paper confers the relative investigation of the consequences of the above-mentioned algorithms are utilized efficiently.
Keywords:Decision Tree model algorithm, Data Mining, Random Forest model Algorithm, Patient, Illness, Light GBM.