The document discusses a machine learning-based system for predicting diabetes risk levels in patients. It emphasizes the development of a decision support system utilizing algorithms like decision tree, neural networks, naive Bayes, and SVM for improved accuracy in diagnostics. The proposed system is designed to efficiently process and analyze diabetes-related data to aid healthcare specialists in early detection and prevention of the disease.

1. Abstract
The diabetes is one of lethal diseases in the world. It is additional a inventor of various varieties
of disorders foe example: coronary failure, blindness, urinary organ diseases etc. In such case the
patient is required to visit a diagnostic center, to get their reports after consultation. Due to every
time they have to invest their time and currency. But with the growth of Machine Learning
methods we have got the flexibility to search out an answer to the current issue, we have got
advanced system mistreatment information processing that has the ability to forecast whether the
patient has polygenic illness or not. Furthermore, forecasting the sickness initially ends up in
providing the patients before it begins vital. Information withdrawal has the flexibility to remove
unseen data from a large quantity of diabetes associated information. The aim of this analysis is
to develop a system which might predict the diabetic risk level of a patient with a better
accuracy. Model development is based on categorization methods as Decision Tree, ANN, Naive
Bayes and SVM algorithms.
Algorithms:
 Decision Tree
 Neural Networks
 Naive Bayes
 SVM algorithms
Existing System:
The prior diabetes isn't therefore great than the traditional worth. Diabetes is appreciations to
either the exocrine gland not manufacturing plentiful hypoglycemic agent not responding
properly to the hypoglycemic agent created. Various information mining algorithms presents
different decision support systems for assisting health specialists. The effectiveness of the
decision support system is recognized by its accuracy. Therefore, the objective is to build a
decision support system to predict and diagnose a certain disease with extreme amount of
precision. The AI consists of ML which is its subfield that resolves the real world difficulties by
"providing learning capability to workstation without supplementary program writing.

2. Proposed System:
The proposed system focuses using algorithms combinations shown above in the block diagram.
The base classification algorithms are: Decision tree, Support Vector Machine, Naive Bayes and
ANN for accuracy authentication.
Fig 1. Block diagram of diabetes prediction system
The dataset contains seven sixty eight instances and nine features. The dataset features are:
 Total number of times pregnant
 Glucose/sugar level
 Diastolic Blood Pressure
 Body Mass Index (BMI)

3.  Skin fold thickness in mm
 Insulin value in 2 hour
 Hereditary factor- Pedigree function
 Age of patient in years
Percentage split option is provided for training and testing.
SYSTEM CONFIGURATION:
Hardware requirements:
Processer : Any Update Processer
Ram : Min 4 GB
Hard Disk : Min 100 GB
Software requirements:
Operating System : Windows family
Technology : Python 3.6
IDE : PyCharm