Nowadays, Diabetes has become a constant chronic disease affecting the mankind. Various causes such as
bacterial or viral infection, toxic or chemical contents mix with the food, auto immune reaction, obesity, bad diet, change in
lifestyles, eating habit, environment pollution, etc. are responsible in increasing number of victims suffering from Diabetes.
Hence, It would be very helpful in predicting this disease at early stage and diagnosing the disease effectively. In health
care, this process is carried out using machine learning algorithms to analyze medical data to build to carry out medical
diagnoses. Diabetes Mellitus or Diabetes is a serious chronic disease which results in increase of blood sugar. It has always
been tedious to identify diabetes, but with emergence of machine learning the identification process has become simpler.
Three machine learning algorithms namely SVM, Decision Tree and Naive Bayes are used to detect Diabetes in earlier
stages. Algorithms are experimented and evaluated on measures like precision, Accuracy-measure and Recall. The results
obtained show Naive Bayes performs better with 76.30% compared to other algorithms. These results are verified using
Receiver Operating Characteristic (ROC) curves in a proper and systematic manner.
Published In:IJCSN Journal Volume 8, Issue 4
Date of Publication : August 2019
Pages : 387-397
Figures :14
Tables : --
Ruchita Gudipati :
currently studying 4th year of engineering in
Computer Science from GITAM University, Hyderabad. I have an
inclination towards the field of research and towards contributing
my part of knowledge especially to the domain of Machine
Learning.
Muvva Vennela Sai :
currently in 4th year pursuing engineering in
Computer Science from GITAM University, Hyderabad. Apart from
my various interests in this field of study, the subject of Machine
Learning has inspired me to research further.
K Radha :
completed her BTECH,MTECH at JNTUH. Pursuing
PhD in KL University,Guntur. She has 12 years of Teaching
Experience and 3 Years of Research Experience. She has applied
for DST Research Projects. She has published 25 papers in
International Journals, SCOPUS journals and Springer and IEEE
Conferences. Her Research Interests are Cloud Computing, Big
Data Analytics, Machine Learning, Deep Learning, and Artificial
Intelligence.
Diabetes has become a chronic disease claiming many
lives hence, detection of it in early stages is vital. In the
conducted study, initiatives are taken to predict diabetes.
Machine learning classification algorithms were used and
Naive Bayes was found to outperform the remaining two
classification algorithms with accuracy over 76.30%.
Results were obtained from Pima Indians Diabetes
Database and classification algorithms. The above results
indicate a promising results and the scope of fiels like
Machine learning and Data mining. In near future,
machine learning classification algorithms can be used to
predict and diagnose other diseases. Automation can be
used to perform better diabetes analysis.
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