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  Implementation of Machine Learning Classifiers for Predicting the Diabetes Mellitus  
  Authors : Ruchita Gudipati; Muvva Vennela Sai; K Radha
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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, Diabetes Mellitus, SVM, Decision Tree, Na´ve Bayes

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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