Lung disease is the most widely recognized malignant growth that kills many individuals across the world. Numerous PC-helped calculations are utilized for the examination of this malignant growth. In this examination, Random Forest and Support Vector Machine (SVM) calculations are utilized to anticipate Lung Cancer. K-NN Algorithm is utilized for pre-handling the dataset and afterward, the current calculations, for example, Random Forest and SVM are applied in the dataset for diagnosing the cellular breakdown in the lungs in the patients, and a recently proposed calculation, Optimized RF Algorithm consolidates the qualities of Random Forest and SVM is utilized in this exploration. When contrasted and the Random Forest calculation and Support Vector Machine, the outcomes show that the Optimized RF Algorithm (Optimized-RF) gives preferable execution over the current calculations.

Topics
Support vector machine, Educational assessment, Diseases and conditions, Organs
1.
Small-Cell Lung Cancer Detection Using a Supervised Machine Learning Algorithm”,
march 2017.
2.
Mohammad
Ismail
,
K. Naga
Lakshmi
,
Y. Kishore
Reddy
,
M.
Kireeti
,
T.
Swathi
 “
Design and Implementation of Student Chat Bot using AIML and LSA
”
International Journal of Innovative Technology and Exploring Engineering (IJITEE)
Volume-
8
Issue-
6
, ISSN: 2278-3075 PP
1742
–
1746
April
2019
Google Scholar
 
3.
Mohammad
Ismail
,
V. Harsha
Vardhan
,
V. Aditya
Mounika
,
K. Surya
Padmini
 “
An Effective Heart Disease Prediction Method Using Artificial Neural Network
”
International Journal of Innovative Technology and Exploring Engineering
’ at Volume-
8
Issue-
8
, pp
1529
–
1532
June
2019
.
Google Scholar
 
4.
T.
Srilatha
,
K.
Subrahmanyam
, K.
Amarendrakyline based Keyword search aware repeated Queries from Incentive Environment
,
International Journal of Innovative Technology and Exploring Engineering (IJITEE),
ISSN: 2278-3075, Volume:
08
, Issue:
07
, pp.
1305
–
1309
, May (
2019
)
Google Scholar
 
5.
Ahmed Iqbal
Pritom
,
Md Ahadur Rahman
Munshi
,
Shahed Anzarus
Sabab
, and
Shihabuzzaman
Shihab
 “
Predicting cancer recurrence using effective classification and feature selection technique
”
In Computer and Information Technology (ICCIT), 2016 19th International Conference
on, pages
310
–
314
. IEEE,
2016
.
Google Scholar
 
6.
S.
Nathiya
,
J.
Sumitha
 “
A Comparative Study on Breast Cancer Prediction using Optimized Algorithms
”,
2021 2nd International Conference on Smart Electronics and Communication (ICOSEC)-
November
2021
.
Google Scholar
 
7.
Kaur H. Wasan
SK
.
Empirical Study on Applications of Data Mining Techniques in Healthcare
.
Journal of Computer Science.
2006
;
2
(
2
):
194
–
200
. Crossref
https://doi.org/10.3844/jcssp.2006.194.200
Google Scholar
 
8.
Hiba
Asri
,
Hajar
Mousannif
,
Hassa
,
AI
Moatassime
, and
Thomas
Noel
.
Using machine learning algorithms for breast cancer risk prediction and diagnosis.
Procedia Computer Science
,
83
:
1064
–
1069
,
2016
https://doi.org/10.1016/j.procs.2016.04.224
Google Scholar
 
9.
K.
Srinivas
,
Dr. Mohammed
Ismail
 B. “
Testcase Prioritization With Special Emphasis On Automation Testing Using Hybrid Framework
”
Journal of Theoretical and Applied Information Technology
Vol.
96
. No
13
4180
–
4190
July
2018
Google Scholar
 
10.
Janee
Alam
,
Sabrina
Alam
,
Alamgir
Hossan
, “
Multi-Stage Lung Cancer Detection and Prediction Using Multi-class SVM Classifier
”,
2018
.
Google Scholar
 
11.
Dania Abed
Aljawad
,
Ebtesam
Alqahtani
,
AL Kuhaili
Ghaidaa
,
Nada
Qamhan
,
Noof
Alghamdi
,
Saleh
Alrashed
,
Jamal
Alhiyafi
, and
Sunday O.
Olatunji
. Breast cancer surgery survivability prediction using bayesian network and support vector machines.
In Informatics, Health & Technology (ICIHT), International Conference on
, pages
1
–
6
. IEEE,
2017
.
Google Scholar
 
12.
Rudra A.
Godse
,
Smita S.
Gunjal
,
Karn A.
Jagtap
,
Neha S.
Mahamuni
,
Multiple Disease Prediction Using Different Machine Learning Algorithms Comparatively
,
2019
,
International Journal of Advanced Research in Computer and Communication Engineering,
Vol
8
:
50
–
52
Google Scholar
 
13.
Naveen
Kishore
 G.,
Rajesh
V.
,
Vamsi Akki
Reddy
,
Prediction of Diabetes Using Machine Learning Classification Algorithms
,
2020
,
International Journal of Scientific & Technology Research,
Vol.
9
:
1805
–
1808
.
Google Scholar
 
14.
Sumitha
J.
,
Mallika
R.
,
Cancer Classification in Microarray Data Using Gene Expression with SVM OAA and SVM OAO
,
International Journal of Advanced Research in Computer Science,
2011
, Vol.
2
:
1
–
4
.
https://doi.org/10.7815/ijorcs.21.2011.008
Google Scholar
 
15.
Sorin Valcan West University of Timişoara, Timişoara, Romania ;MihailGăianu, Lung Tumor Segmentation Accelerated by CUDA, IEEE.
16.
Sumitha
J.
,
Devi T.
Ravi
 D.,
Comparative Study on Gene Expression for Detecting Diseases Using Optimized Algorithm
,
International Journal of Human Genetics,
2017
, Vol.
17
:
38
–
42
.
https://doi.org/10.1080/09723757.2017.1305722
Google Scholar
 
17.
Sumitha
J.
,
Analysis of Gene Expression Value Using Bio-Inspired Algorithms
,
Pakistan Journal of Biotechnology,
2019
, Vol.
16
:
115
–
118
.
Google Scholar
 
18.
Gyeongcheol
Cho
, JinyeongYim,
Review of Machine Learning Algorithms for Diagnosing Mental Illness
,
Psychiatry Investing.
2019
, Vol.
16
:
262
–
269
.
https://doi.org/10.30773/pi.2018.12.21.2
Google Scholar
 
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