The document outlines a disease prediction system using supervised learning to enhance medical attention by predicting diseases based on symptoms. It describes the use of machine learning algorithms, specifically the Naïve Bayesian algorithm, and mentions the dataset sourced from a study at Columbia University. The implementation involves GUI development with Tkinter and data handling using NumPy and Pandas to prepare symptom and disease lists for analysis.

1. Disease Prediction by
System using
Supervised Learning

2. Problem Definition
 Predicts diseases based on symptoms (in order to improve
medical attention given to patients)
 Classical Diagnosis
 Machine Learning algorithms use a computer aided
prediction can be made by inputting the symptoms.

3. Working
Fig: Block diagram for Disease Prediction by System using
Supervised Learning

4. Dataset
The dataset for this project is taken from a study conducted at Columbia University.

5. Naïve Bayesian algorithm
Features :
1. Touch Screen
2. Internet facility
3. Good camera, etc.
I am a
Smart Phone

6. Working 1 : Initial part
 Import all the packages required i.e. Tkinter for GUI , numpy to perform
numerical operations and pandas for reading the csv files.
 Create a list which contains all the symptoms which are according the
csv file
 Create another list which contains the diseases.
 Then, create a empty list
L1 and L2 , both have equal length.
L1 Sym1 sym2 sym3 sym4 sym5 sym6 sym7 sym8 ……..
L2 0 0 0 0 0 0 0 0 ……..

7. Working 2 : Dataset part
Perform same steps for both testing and training dataset
1. Using pandas read the CSV file
2. Replace with index
3. Make X as symptoms and Y as disease.
S1 S2 S3 S4 S5 S6 S7 S8 …… Prognosis
0 0 0 1 0 1 0 0 ……. 0
0 1 0 0 0 0 0 0 ……. 1
1 0 0 0 0 0 0 1 ……. 2
0 0 0 0 0 0 0 0 ……. 3

8. THANK YOU