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ABSTRACT
The health care systems collects data and reports from the hospitals or patient’s
database by machine learning and data processing techniques which is employed to
predict the disease so as to create reports supported the results which used for
various kinds of predictions for disease and which is that the leading explanation for
the human’s death since past years. Medical reports and data had been extracted
from various databases to predict a number of the required diseases which are
commonly found in people nowadays breast cancer, heart disease and diabetes
disease and make their life more critical to measure. Nowadays technology
advancement within the health care industry has been helping people to create their
process easier by suggesting hospitals and doctors to travel to for his or her
treatment, where to admit and which hospitals are the simplest for the treating the
desired disease. we’ve implemented this sort of system in our application to form
people’s life simpler by predicting the disease by inputting certain data from their
reports which can give the result positive or negative supported the disease prediction
they are going to be having a choice to get recommendation of best hospitals with
best doctors nearby from the past users or guardians..
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LIST OF FIGURES
FIGURE NO. FIGURE NAME. PAGE NO.
4.1 Heart disease data set details. 10
4.2 Correlation formula. 12
4.3 Diabetes disease data set details. 12
4.4 Breast cancer data set details. 14
4.5 System Architecture. 15
4.6 Flowchart Diagram. 17
4.7 Algorithm selection for diabetes disease prediction. 21
4.8 Algorithm selection for heart disease prediction. 21
4.9 Algorithm selection for breast cancer prediction. 22
4.10 Logistic regression graph. 24
4.11 Random Forest algorithm architecture. 27
5.1 Diabetes disease prediction result. 30
5.2 Heart disease prediction result. 30
5.3 Breast cancer prediction result. 31
5.4 Performance analysis of logistic regression
For diabetes disease prediction. 32
5.5 Performance analysis of Random Forest classifier
For heart disease prediction. 32
5.6 Performance analysis of Random Forest classifier
For breast cancer prediction. 33
7.1 Plagiarism Report 1 52
7.2 Plagiarism Report 2 53
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LIST OF TABLES
TABLE NO. TABLE NAME. PAGE NO.
4.1 Hardware Requirements 8
4.2 Software Requirements 8
4.3 Heart disease data set fields 10
4.4 Diabetes disease data set fields 11
4.5 Breast cancer data set fields 14
