Cancer prediction

1. A Fast and Accurate Prediction of
Cancer using machine learning
Presented by,
C.Anantha
Devi(960519104007)
G.S.Abarna(960519104001)
P.Mageshwari(96051910404
0)
Guided by,
Mrs.Reema HOD/IT

2. Scope & objective
• Increase quality and years of healthy life.
• Eliminate health disparities.

3. Abstract
 The mortality rate of cancer is among the
highest in the world. One death occurs every
six in the world. Both machine learning
(ML) and deep learning (DL) have been used
by scientists to predict cancer.
 In addition, DL can analyze a huge amount
of healthcare data in a short period of time to
study the chances of recurrence, progression
and patient survival.
 A fast and accurate optimizer is necessary to
predict both critical and non-critical cases.

4. Existing system
 Developing a prediction model from risk
factors can provide an efficient method to
recognize breast cancer. Data mining
techniques have been applied to increase the
efficiency of diagnosis at the early stage.
 A support vector machine (SVM) combined
with an extremely randomized trees classifier
(extra-trees) to provide a diagnosis of breast
cancer at the early stage based on risk
factors. The extra-trees classifier was used to
remove irrelevant features, while SVM was
utilized to diagnose the breast cancer status.

5. DisAdvantages
 Lack of early diagnosis
 Lack of accuracy
 Lack of Sensitivity

6. Proposed system
 Machine learning (ML) techniques play a key
role in healthcare in recent years.
 In the case of breast cancer, machine
learning techniques can be used to
distinguish between malignant and benign
tumours for enabling early detection.
 Most ML based applications focus on large
data sets citing ML’s ability to handle big
data.
 However, from a user’s perspective most
users have access to publicly available small
data sets.
 Thus, it is interesting to analyse if the
traditional non complex basic ML algorithms
can achieve high accuracy classifications

7. Advantage
 Propose a fast, accurate, and scalable
framework.
 Reduced dataset size.
 enhancing cancer prognostic
prediction while also lowering the bulk
of the input data.

8. References
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Clinicians, vol. 69, no. 5, pp. 7–30, 2019.
 [2] H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and
F. Bray, ‘‘Global cancer statistics 2020: GLOBOCAN estimates of incidence and
mortality worldwide for 36 cancers in 185 countries,’’ CA, Cancer J. Clinicians, vol.
71, no. 3, pp. 209–249, May 2021, doi: 10.3322/caac.21660.
 [3] I. Hameed, S. R. Masoodi, P. A. Malik, S. A. Mir, K. Ghazanfar, and B. A.
Ganai, ‘‘Genetic variations in key inflammatory cytokines exacerbates the risk of
diabetic nephropathy by influencing the gene expression,’’ Gene, vol. 661, pp. 51–
59, Jun. 2018, doi: 10.1016/j.gene.2018.03.095.
 [4] S. Behjati and P. S. Tarpey, ‘‘What is next generation sequencing?’’Arch.
Disease Childhood Educ. Pract. Ed., vol. 98, no. 6, pp. 236–238, Dec. 2013, doi:
10.1136/archdischild-2013-304340.
 [5] L. Ding, M. C. Wendl, D. C. Koboldt, and E. R. Mardis, ‘‘Analysis of next-
generation genomic data in cancer: Accomplishments and challenges,’’ Hum. Mol.
Genet., vol. 19, no. 2, pp. 188–196, 2010, doi: 10.1093/hmg/ddq391.

9. Thank You!!!