Multiple Disease Prediction using Machine Learning

Abstract

In this digital world most of the people are having lots of health related issues. It is very important to know if we are suffering from an illness, at an early staged disease rather than discovering it at a later. So on-time analysis of any health related issues is important for the prevention and treatment of the illness. In this study we have developed a system for the prediction of multiple diseases like heart, diabetes, Parkinson’s disease. By which patient can predict multiple diseases on single platform. This disease prediction system uses ML algorithm named as Support vector machine.

Introduction

I. INTRODUCTION

When anyone is currently afflicted with a disease, they must see a doctor, which is both time consuming and expensive. It can also be difficult for the user if they are out of reach of doctors and hospitals because the illness cannot be detected. So, if the above procedure can be done using automated software that saves time as well as money, it could be better for the patient, making the process go more smoothly.

Lots people are always use internet to learn new things, particularly as the use of the internet grows every day. When an issue occurs, people often want to look solution for it up on the internet. Hospitals and doctors have less access to the internet than the patients. When a person gets affected with an illness, they do not have many options to find the solutions. As a result, this system can be beneficial to patient.

Machine Learning is a subset of Artificial Intelligence. Machine learning mainly deals with the study of algorithms which improve with the use of data. Machine Learning has two phases i.e. Training and Testing. There are two kinds of Machine Learning – Supervised Learning and Unsupervised Learning. In supervised learning we develop a model with the help of data that is well labelled.

The intent is to find a satisfactory Machine Learning algorithm which is efficient and accurate for the prediction of disease. In this paper, the supervised Machine Learning is used for predicting the diseases. The main feature will be Machine Learning in which we will be using algorithms such as Support vector machine. This will help in early prediction of diseases accurately.

In this system, we are going to predict Diabetes, Heart, and Parkinson’s disease. Later we can add many more diseases. To develop multiple disease prediction systems we are going to use machine learning algorithms such as SVM. Django and Python pickling is used to save the behavior of the model. In this prediction system all the parameters which are important for the predicting the disease are included so it possible to predict the disease efficiently and more accurately.

II. PROBLEM STATEMENT

Many of the existing machine learning models for health care analysis is focusing on one disease per analysis. For example first is for heart disease analysis, one for diabetes analysis, one for Parkinson’ diseases like that. If a user wants to predict more than one disease, he has to go through different platforms.

There is no common system which can perform analysis on more than one disease on one platform. Some of the models have lower accuracy which can seriously affect patients’ health. When a doctor wants to analyse their patient’s health reports, they have to use many models which is turn into increase in cost as well as time.

III. PROPOSED SYSTEM

In the proposed system, we have built multiple disease prediction system using a Machine Learning algorithm that is Support vector machine. Based on the patient symptoms that are entered by the user the disease is predicted.  In this prediction system we are going to predict heart disease, diabetes, Parkinson’s disease.

In multiple disease prediction, there is possible to predict multiple diseases at a time. So by which user doesn’t need to go for multiple systems which will save the time and money.

IV. LITERATURE REVIEW

According to the paper, this paper focuses about as diabetes is one of the dangerous diseases in the world. Diabetes can cause many varieties of disorders to the patients which includes blindness etc. In this paper author has used machine learning algorithms to find out diabetes disease.

This research paper was written by Divya Mandem, B. Prajna to provide a survey of existing techniques of data mining techniques using databases that are used in today's medical research, specifically in Heart Disease Prediction. In this study numbers of experiments have been carried out to compare the performance of predictive data mining techniques on the same dataset.

According to the paper, this paper aimed to predict heart diseases using supervised ML techniques. The authors structured patient’s data by using the attributes as gender, age, chest pain, gender, target and slope. The applied ML algorithms that were deployed are SVM.

According to this paper, the Parkinson’s disease is one of the serious diseases which happened to the older peoples. Early detection of the disease is very important to monitor the patient health. In this paper author has used different machine learning algorithms to predict the disease. Their aim was to build the Parkinson’s disease prediction system which can help to the older people.

V. SYSTEM REQUIREMENT

A. Software Requirements

1. Operating System : Windows 11
2. Front End : HTML, CSS, Bootstrap, JavaScript
3. Back End : Python, Django
4. IDE : VS Code
5. Database : MySQL

B. Hardware Requirements

1. Processor : Intel Core I3 or Higher
2. RAM : 4 GB
3. Hard Disk : 1 TB
4. SDD : 128 GB

VI. DESIGN

A. Architecture Design

In the Fig. 6.1, we have experimented on three diseases that are heart, diabetes and Parkinson’s disease. The first step is to get registered to get access for the dashboard of our platform. After registering successfully user will redirected to the dashboard where user will able to see the three diseases that are heart disease, diabetes and Parkinson’s disease.

After selecting the disease patient will feed the symptoms that are required for the prediction.  In next step we have imported the dataset for heart disease, diabetes disease and Parkinson’s disease respectively. Once we have imported the dataset then visualization of each inputted data takes place. After visualization the next step pre-processing of data takes place where we check outliers, missing values and also scale the dataset then on the updated dataset we split the data into training and testing. Next is on the training dataset we had applied SVM algorithm. Then we build a pickle file for all the disease. The pickle file is integrated with the Django framework for the output of the model on the webpage.

B. User Interface Design

VII. IMPLEMENTATION

A. Support Vector Machine Algorithm

The SVM algorithm can be understand by focusing on its primary type SVM Classifier. The idea behind the SVM classifier is to come up with a hyper plane in N Dimensional space that divides the data points belonging to different classes. However, this hyper-plane is chosen based on margin as the hyper plane providing maximum margin between the two classes is considered. These margins are calculated using data point known as support vector. Support vector are the data points that are near to the hyper plane.

1. Step 1: SVM algorithm predicts the classes. One class is identified as 1 and another class as -1.
2. Step 2: In SVM classifier, a loss function known as the hinge loss function is used to find the maximum margin.
3. Step 3: Loss function can also be called a cost function where cost is 0 when no class is incorrectly predicted.  If this is not the case then error is calculated.
4. Step 4: In most of the optimization problems, weights are optimized by calculating the gradients using advanced mathematical concepts of partial derivatives.
5. Step 5: The gradients are updated only by using the regularization parameter when there is no error in the classification while the loss function is also used when misclassification happens.

VIII. RESULT

In the system we have used SVM algorithm for the prediction. When the patient will input the values in system according to that it will show whether the patient has a disease or not. The parameters will show the range of the values needed. The entered value is not between the ranges or is not valid or is empty it will show the warning sign that add a correct value.

X. FUTURE SCOPE

In future we can add more disease in exiting prediction system. We can try to increase the accuracy of prediction of disease to reduce the morality. Also we can try to make the system more user friendly by adding new features.

XI. ACKNOWLEDGEMENT

We sincere thanks to our college Dhole Patil College of Engineering, Pune for giving us a platform to prepare a project on the topic "Multiple Disease Prediction using machine Learning". We are sincerely grateful for Prof. Archana Priyadarshni as our guide and Dr. Aarti Dandawate, Head of Computer Engineering Department, for providing help during our research, which would have seemed difficult without their motivation, constant support, and valuable suggestion.

Conclusion

Predicting the disease earlier can improve the patients’ health. The aim of this project is to predict multiple diseases based on symptoms. The project is built in such a way that the system takes the patients symptoms as input and generates an output, which is nothing but the disease prediction. This model can help to reduce the cost required in dealing with this disease and also help to improve the recovery process. By using this system patient can reduce the money required for treatment and can save the time.

References

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Copyright

Copyright © 2023 Abhishek Pattar , Sameer Kurhade , Manoj Salunke , Darshan Satav, Prof. Archana Priyadarshni . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.