MEDICAL COST PREDICTION USING

MACHINE LEARNING
INTERNSHIP R E P O R T
Submitted by

SANTHOSH S
[EA*******10127]

Under the Guidance of

Dr.G.Babu

(Assistant Professor, Directorate of Online Education)

in partial fulfillment for the award of the degree of

MASTER OF COMPUTER APPLICATIONS

DIRECTORATE OF ONLINE EDUCATION

SRM INSTITUTE OF SCIENCE AND TECHNOLOGY
KATTANKULATHUR- 603 203

June 2025
 DIRECTORATE OF ONLINE EDUCATION

SRM INSTITUTE OF SCIENCE AND TECHNOLOGY

KATTANKULATHUR – 603 203

BONAFIDE CERTIFICATE

This Internship report titled “MEDICAL COST PREDICATION

USING MACHINE LEARNING” b y SANTHOSH S [EA**********10127] who

carried out the Internship Work under my supervision along with the company mentor.

Certified further, that to the best of my knowledge, the work reported herein does not form any

other internship report or dissertation based on which a degree or award was conferred on an

earlier occasion on this or any other candidate

1
 INTERNSHIP OFFER LETTER

CERTIFICATE OF INTERNSHIP WORK

THIS IS TO CERTIFY THAT SANTHOSH S

[EA************127] HAS UNDERGONE INTERNSHIP WORK FROM 20-02-2025

TO 06-05-2025 UNDER THE GUIDANCE OF R.S. ARUN

THIS WORK HAS BEEN CARRIED OUT IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE AWARD OF THE DEGREE OF MASTER OF COMPUTER APPLICATIONS

2
 ACKNOWLEDGEMENTS

We express our humble gratitude to Dr C. Muthamizhchelvan, Vice-Chancellor,

SRM Institute of Science and Technology, for the facilities extended for the project work and his

continued support. We extend our sincere thanks to Director DOE, SRM Institute of Science and

Technology, Prof. Dr Manoranjan Pon Ram, for his invaluable support. SRM Institute of

Science and Technology, for her support throughout the project work. We want to convey our

thanks to Programme Coordinator Dr. G. Babu, Directorate of online Education, SRM Institute

of Science and Technology, for their inputs during the project reviews and support. Our

inexpressible respect and thanks to my guide, Dr G.Babu., Assistant Professor & Programme

Coordinator Directorate of online Education,, SRM Institute of Science and Technology, for

providing me with an opportunity to pursue my project under her mentorship.

He provided me with the freedom and support to explore the research topics of my

interest. His passion for solving problems and making a difference in the world has always been

inspiring. We sincerely thank the Directorate of online Education, staff and students, SRM

Institute of Science and Technology, for their help during our project. Finally, we would like to

thank parents, family members, and friends for their unconditional love, constant support, and

encouragement.

NAME OF STUDENT

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 TABLE OF CONTENTS

INTERNSHIP OFFER LETTER ............................................................... 2

1. ABSTRACT .......................................................................... 6

2. INTRODUCTION.................................................................. 7

2.1 Objective................................................................................ 7

2.2 Existing System ..................................................................... 7

2.3 Proposed System .................................................................... 8

3. SYSTEM REQUIREMENTS ................................................. 9

3.1 Hardware Requirement Specification...................................... 9

3.2 Software Requirement Specification ....................................... 9

4. MACHINE LEARNING ...................................................... 10

4.1 Machine Learning vs. Traditional Programming ................... 10

4.2 How does Machine learning work?....................................... 10

4.3 Machine learning Algorithms and where they are used ......... 13

5. PYTHON OVERVIEW ....................................................... 14

6. ANACONDA NAVIGATOR ............................................... 15

6.1 Why use Navigator? ............................................................. 15

6.2 What applications can I access using navigator? ................... 16

6.3 How can I run code with Navigator? .................................... 16

7. SYSTEM ARCHITECTURE ............................................... 17

7.1 Block Diagram: .................................................................... 17

7.2 Methodology ........................................................................ 18

8. System Modules ................................................................... 18

8.1 Data Ingestion ........................................................................... 18

8.2 Data Preprocessing ............................................................... 19

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 8.3 Exploratory data analysis ..................................................... 19

8.4 Data Splitting and Modelling ................................................ 19

8.5 Evaluation Metric................................................................. 20

9. SOURCE CODING ............................................................. 21

9.1 App.py ................................................................................. 21

9.2 medical_cost_checkpoint.ipynb ............................................ 21

10. RESULT .............................................................................. 24

10.1 Dataset ................................................................................. 24

10.2 Data Preprocessing ............................................................... 24

10.3 Data Exploration .................................................................. 25

10.4 RF Model Result .................................................................. 25

10.5 LR Model Result .................................................................. 26

10.6 DTree Model Result ............................................................. 26

10.7 Home page ........................................................................... 27

11 CONCLUSION................................................................................... 28

REFERENCES ........................................................................................ 29

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 1. ABSTRACT

Health care costs increase day by day. As there are a greater number of new viruses entering into

people, there is a need to predict health charges. This type of prediction helps the governments to

make a decision regarding health issues. People also know the importance of health care costs.

Machine Learning is a field which has its impact on every field. Health care system also uses

machine learning models for several health related applications. In this paper, we have done

predicate analysis on medical health insurance charges. We build a model to predict the medical

insurance cost of a person based on gender. We collect the dataset from Kaggle, which contains

1338 rows of data with the features age, gender, smoker, BMI, children, region, and insurance

charges. The data contains medical information and costs billed by health insurance companies. We

applied various regression algorithms on this dataset to predict medical costs.We live on a planet

that is filled with dangers and uncertainties. People, households, businesses, properties, and property

are all vulnerable to various types of risk, and the degree of risk can differ. These Death, illness, and

property loss are all potential threats and assets.

The most important aspects of people's lives are their health and happiness lives. However, because

dangers cannot always be avoided, the world of Finance has created a slew of tools to protect you.

Individuals and organizations can be protected against these dangers by employing. They will be

reimbursed with financial capital. As a result, insurance is a need. Policy that reduces or eliminates

the expenses of loss incurred by various risks, as well as the importance of insurance in people's

lives It is crucial for businesses to understand the needs of individuals. When it comes to the value

of insurance in people's lives, it's critical for insurance firms to be able to accurately evaluate or

quantify the amount covered by the policy and the insurance payments that must be paid.

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 2. INTRODUCTION

We live on a planet that is filled with dangers and uncertainties. People, households,

businesses, properties, and property are all vulnerable to various types of risk, and the degree of risk

can differ. These Death, illness, and property loss are all potential threats and assets. The most

important aspects of people's lives are their health and happiness lives. However, because dangers

cannot always be avoided, the world of Finance has created a slew of tools to protect you.

Individuals and organizations can be protected against these dangers by employing. They will be

reimbursed with financial capital. As a result, insurance is a need. Policy that reduces or eliminates

the expenses of loss incurred by various risks, as well as the importance of insurance in people's

lives It is crucial for businesses to understand the needs of individuals. When it comes to the value

of insurance in people's lives, it's critical for insurance firms to be able to accurately evaluate or

quantify the amount covered by the policy and the insurance payments that must be paid.

2.1 Objective
Predict the future medical expenses of subjects based on certain features building a robust machine
learning model. Identifying the factors affecting the medical expenses of the subjects based on the
model output.

2.2 Existing System

Previously they applied various supervised and unsupervised models for predictive analysis. They
also suggested machine learning tools and techniques are decisive in health care province and
exclusively used in the diagnosis and predictions of various types of cancers. One approach applied
hierarchical decision tress for medical price prediction system. Their experiments shown that the
price prediction system achieves 70% accuracy.

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Drawbacks:

 Time consumption is more for loading the data.

 Accuracy level is also not up to the satisfied level.

2.3 Proposed System

The dataset used for experiments is collected from Kaggle[6] machine learning repository. This
dataset was inspired by the book Machine Learning with R by Brett Lantz. The data contains medical
information and costs billed by health insurance companies. It contains 1338 rows of data and the
following columns: age, gender, BMI, children, smoker, region, insurance charges. In these features
insurance charges are a dependent variable and the remaining features are called as independent
variables. In regression analysis, we need to predict the value of dependent variables using
independent variables. First, we collected a dataset and applied various data preprocessing methods.

Data preprocessing is a technique in which we can remove missing values in the data. Because of
these missing values, it is not possible to apply machine learning algorithms. After removal of
missing values, we need to apply label encoding, one hot encoding data to the categorical features.
Categorical features are the features whose values are labels instead of values. After that, apply
standardization or normalization techniques to our data. This method is used when all the attribute
values are not in the same scale.

In regression analysis, we need to predict the value of dependent variables using independent
variables. First, we collected a dataset and applied various data preprocessing methods. Then we
applied the following four regression models on the dataset.
i) Linear Regression

ii) Support Vector Regression

iii) Decision Tree Regression

iv) Random Forest Regression

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 3. SYSTEM REQUIREMENTS

3.1 Hardware Requirement Specification

 Processor - Intel Processor

 RAM - 1 GB Or more

 Hard Disc - 20 GB or more

 Monitor - 15” color monitor or advance

 Keyboard - Any Keyboard

 Mouse - Any mouse

 Printer - In case of printing reports

3.2 Software Requirement Specification

 Operating System - Windows OS

 Language - Python

 Browser - Any of Mozilla, Opera, Chrome etc.

 Framework - FLASK

 Application - Visual Studio Code

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 4. MACHINE LEARNING
Machine Learning is a system that can learn from example through self-
improvement and without being explicitly coded by programmers. The breakthrough comes with
the idea that a machine can singularly learn from the data (i.e., example) to produce accurate
results. Machine learning combines data with statistical tools to predict an output. This output is
then used by corporations to make actionable insights. Machine learning is closely related to data
mining and Bayesian predictive modeling. The machine receives data as input, use an algorithm to
formulate answers.

A typical machine learning task is to provide a recommendation. For those

who have a Netflix account, all recommendations of movies or series are based on the user's
historical data. Tech companies are using unsupervised learning to improve the user experience
with personalizing recommendations. Machine learning is also used for a variety of tasks like
fraud detection, predictive maintenance, portfolio optimization, automatizing tasks and so on.

4.1 Machine Learning vs. Traditional Programming

Traditional programming differs significantly from machine learning. In traditional programming,
a programmer codes all the rules in consultation with an expert in the industry for which software
is being developed. Each rule is based on a logical foundation; the machine will execute an output
following the logical statement. When the system grows complex, more rules need to be written. It
can quickly become unsustainable to maintain.

DATA RULES
COMPUTER

OUTPUT

Machine Learning

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 4.2 How does Machine learning work?

Machine learning is the brain where all the learning takes place. The way the machine learns is
similar to the human being. Humans learn from experience. The more we know, the more easily we
can predict. By analogy, when we face an unknown situation, the likelihood of success is lower than
the known situation. Machines are trained the same. To make an accurate prediction, the machine
sees an example. When we give the machine a similar example, it can figure out the outcome.
However, like a human, if it feeds a previously unseen example, the machine has difficulties to
predict.
The core objective of machine learning is the learning and inference. First of all, the machine
learns through the discovery of patterns. This discovery is made thanks to the data. One crucial part
of the data scientist is to choose carefully which data to provide to the machine. The list of attributes
used to solve a problem is called a feature vector. You can think of a feature vector as a subset of
data that is used to tackle a problem.

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The machine uses some fancy algorithms to simplify the reality and transform this discovery into
a model. Therefore, the learning stage is used to describe the data and summarize it into a model.

For instance, the machine is trying to understand the relationship between the wage of an individual
and the likelihood to go to a fancy restaurant. It turns out the machine finds a positive relationship
between wage and going to a high-end restaurant: This is the model
When the model is built, it is possible to test how powerful it is on never-seen-before data. The new
data are transformed into a features vector, go through the model and give a prediction. This is all
the beautiful part of machine learning. There is no need to update the rules or train again the model.
You can use the model previously trained to make inference on new data.

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The life of Machine Learning programs is straightforward and can be summarized in the following
points:

1. Define a question
2. Collect data
3. Visualize data
4. Train algorithm
5. Test the Algorithm
6. Collect feedback
7. Refine the algorithm
8. Loop 4-7 until the results are satisfying
9. Use the model to make a prediction

Once the algorithm gets good at drawing the right conclusions, it applies that knowledge to new sets
of data.

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 4.3 Machine learning Algorithms and where they are used

Machine learning can be grouped into two broad learning tasks: Supervised and Unsupervised.
There are many other algorithms
Supervised learning
An algorithm uses training data and feedback from humans to learn the relationship of given inputs
to a given output. For instance, a practitioner can use marketing expense and weather forecast as
input data to predict the sales of cans.
You can use supervised learning when the output data is known. The algorithm will predict new
data.
There are two categories of supervised learning:

● Classification task
● Regression task

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 5. PYTHON OVERVIEW
Python is a high-level, interpreted, interactive and object-oriented scripting language. Python is

designed to be highly readable. It uses English keywords frequently whereas other languages use

punctuation, and it has fewer syntactic constructions than other languages.

o Python is interpreted: Python is processed at runtime by the interpreter. You do not need
to compile your program before executing it. This is similar to PERL and PHP.
o Python is Interactive: You can actually sit at a Python prompt and interact with the
interpreter directly to write your programs.
o Python is Object-Oriented: Python supports Object-Oriented style or technique of
programming that encapsulates code within objects.
o Python is a Beginner's Language: Python is a great language for the beginner-level
programmers and supports the development of a wide range of applications from simple text
processing to WWW browsers to games.

Python's features include:

Easy-to-learn: Python has few keywords, simple structure, and a clearly defined syntax. This
allows the student to pick up the language quickly.
Easy-to-read: Python code is more clearly defined and visible to the eyes.
Easy-to-maintain: Python's source code is fairly easy-to-maintain.
A broad standard library: Python's bulk of the library is very portable and cross-platform
compatible on UNIX, Windows, and Macintosh.
Interactive Mode: Python has support for an interactive mode which allows interactive testing and
debugging of snippets of code.
Portable: Python can run on a wide variety of hardware platforms and has the same interface on all
platforms.
Extendable: You can add low-level modules to the Python interpreter. These modules enable
programmers to add to or customize their tools to be more efficient.
Databases: Python provides interfaces to all major commercial databases.
GUI Programming: Python supports GUI applications that can be created and ported to many
system calls, libraries, and windows systems, such as Windows MFC, Macintosh, and the X

15
Window system of UNIX.
Scalable: Python provides a better structure and support for large programs than shell scripting.

Apart from the above-mentioned features, Python has a big list of good features, few are listed
below:

o IT supports functional and structured programming methods as well as OOP.

o It can be used as a scripting language or can be compiled to byte-code for building large
applications.
o It provides very high-level dynamic data types and supports dynamic type checking.
o IT supports automatic garbage collection.
o It can be easily integrated with C, C++, COM, ActiveX, CORBA, and Java.

Python is available on a wide variety of platforms including Linux and Mac OS X. Let's understand
how to set up our Python environment.

6. ANACONDA NAVIGATOR

Anaconda Navigator is a desktop graphical user interface (GUI) included in Anaconda distribution
that allows you to launch applications and easily manage conda packages, environments and
channels without using command-line commands. Navigator can search for packages on Anaconda
Cloud or in a local Anaconda Repository. It is available for Windows, Mac OS and Linux.

6.1 Why use Navigator?

In order to run, many scientific packages depend on specific versions of other packages.
Data scientists often use multiple versions of many packages, and use multiple environments to
separate these different versions.

The command line program condo is both a package manager and an environment manager, to help
data scientists ensure that each version of each package has all the dependencies it requires and
works correctly.

Navigator is an easy, point-and-click way to work with packages and environments without needing
to type condo commands in a terminal window. You can use it to find the packages you want, install
them in an environment, run the packages and update them, all inside Navigator.
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 6.2 What applications can I access using navigator?
The following applications are available by default in Navigator:

● JupyterLab
● Jupyter Notebook
● QT Console
● Spyder
● VS Code
● Glue viz
● Orange 3 App
● Rodeo
● RStudio
Advanced condo users can also build your own Navigator applications

6.3 How can I run code with Navigator?

The simplest way is with Spyder. From the Navigator Home tab, click Spyder, and write and execute
your code.

You can also use Jupyter Notebooks the same way. Jupyter Notebooks are an increasingly popular
system that combine your code, descriptive text, output, images and interactive interfaces into a
single notebook file that is edited, viewed and used in a web browser.

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 7. SYSTEM ARCHITECTURE

7.1 Block Diagram:

Pre- Data Scaling

Raw Data Processing & Extraction

ML Evaluate
modelling Result

17
 7.2 Methodology

Step 1:-Initialize the dataset containing training data wholesale price index

Step 2:-Select all the rows and column 1 from dataset to “x” Which is independent variable

Step 3:-Select all of the rows and column 2 from dataset to “y” Which is dependent variable

Step 4:- Fit DTR/SVR/LR/RF to the dataset

Step 5:-Predict the new value

Step 6:-Visualize the result and check the accuracy

8. System Modules
8.1 Data Ingestion
Data ingestion is the transportation of data from assorted sources to a storage medium where it can
be accessed, used, and analyzed by an organization. The destination is typically a data warehouse,
data mart, database, or a document store. Sources may be almost anything – including SaaS data,
in-house apps, databases, spreadsheets, or even information scraped from the internet. The data
ingestion layer is the backbone of any analytics architecture. Downstream reporting and analytics
systems rely on consistent and accessible data. There are different ways of ingesting data, and the
design of a particular data ingestion layer can be based on various models or architectures.

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 8.2 Data Preprocessing:
Data Preprocessing is a data mining technique used to transform the raw data into useful and

efficient format. The data here goes through 2 stages 1. Data Cleaning: It is very important for data

to be error free and free of unwanted data. So, the data is cleansed before performing the next steps.

Cleansing of data includes checking for missing values, duplicate records and invalid formatting

and removing them. 2. Data Transformation: Data Transformation is transformation of the datasets

mathematically; data is transformed into appropriate forms suitable for data mining process. This

allows us to understand the data more keenly by arranging the 100‟s of records in an orderly way.

Transformation includes Normalization, Standardization, and Attribute Selection.

8.3 Exploratory data analysis:

Exploratory data analysis (EDA) is an approach to understand the datasets more keenly by means

of visual elements like scatter plots, bar plots, etc. This allows us to identify the trends in the data

more accurately and to perform analysis accordingly. From the yearly trends graphs it is observed

that US Exports depend on and follow the areas planted and harvested annually. A sudden drop in

China‟s Exports in the year 2009 is observed and in the meantime its imports kept increasing in the

last 12 years regardless of the global yield, which implies China has a huge and lasting demand of

soybean crop but now it relies on the global supply to meet the needs.

8.4 Data Splitting and Modelling

Modelling of data involves creating a data model for the data to be stored in the database. The
process of modeling means training a Machine Learning Algorithm to predict the labels from the
features, tuning it for business need, and validating it on the hold out data. The output from modeling
19
is a trained model that can be used for inference, making predictions on new data points. Modeling
is independent of the previous steps in the Machine Learning process and has standardized inputs
which means we can alter the prediction problem without needing to rewrite all our code. If the
business requirements change, we can generate new label times, build corresponding features, and
input them into the model.

Regressors used for prediction purpose –

o Random Forest Regressor- regression method

o Support Vector Regression (SVR) – uses kernel functions
o Linear Regression – regression method
o Decision Tree Regression – regression method

8.5 Evaluation Metric

Models are implemented and later evaluated for their accuracies using root mean square error
Since this is multi classification problem, we use the following metrics:
o R2 score - The r2-score of a regression is the percentage of the test set tuples that are
correctly classified by the regressor.

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 9. SOURCE CODING

9.1 App.py

import numpy as np
import pandas as pd
from flask import Flask, request, jsonify, render_template
import pickle

app = Flask( name ,template_folder='templates')

model = pickle.load(open('model.pkl', 'rb'))

@app.route('/')
def home():
return render_template('index.html')

@app.route('/predict',methods=['POST'])
def predict():
'''
For rendering results on HTML GUI
'''
float_features = [float(x) for x in request.form.values()]

final_features = [np.array(float_features)]
prediction = model.predict( final_features )

output=round(prediction[0],1)

return render_template('index.html', prediction_text='charges Rs. {}'.format(output))

if name == " main ":

app.run(debug=True)

9.2 medical_cost_checkpoint.ipynb
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

data=pd.read_csv('insurance.csv')

data.head()

data.info()
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data.shape

data.columns

## Pre-processing

from sklearn.preprocessing import LabelEncoder

lab=LabelEncoder()

data['sex']=lab.fit_transform(data['sex'])
data['smoker']=lab.fit_transform(data['smoker'])
data['region']=lab.fit_transform(data['region'])

data.head()

## Data Exploration

sns.countplot(x='smoker',data=data)

sns.countplot(x='sex',data=data)

## Data Splitting

x=data.iloc[:,data.columns!='charges']
y=data.iloc[:,data.columns=='charges']

#x.shape

#x.head()

x.head()

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score,confusion_matrix,recall_score, roc_curve, auc

xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.3)

xtrain.head()

ytrain.head()

## RandomForestRegressor

from sklearn.ensemble import RandomForestRegressor

regressor = RandomForestRegressor(n_estimators = 300, random_state = 0) #N-estimators means how many time
predict the value like that
regressor.fit(xtrain, ytrain)

y_pred = regressor.predict(xtest)

#y_pred

from sklearn import metrics

from sklearn.metrics import r2_score

print("\n\nr2_score is " , r2_score(y_pred,ytest))

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## LinearRegression

from sklearn.linear_model import LinearRegression

alg = LinearRegression()
alg.fit(xtrain, ytrain)

y_predict = alg.predict(xtest)

print("\n\nr2_score is " , r2_score(y_predict,ytest))

## DecisionTreeRegressor

from sklearn import tree

dt=tree.DecisionTreeRegressor()
dt.fit(xtrain, ytrain)
x_predicted=dt.predict(xtest)
print("\n\nr2_score is " , r2_score(x_predicted,ytest))

## SVR

from sklearn.svm import SVR

regressor = SVR()
regressor.fit(xtrain, ytrain)
predicted=regressor.predict(xtest)
print("\n\nr2_score is " , r2_score(predicted,ytest))

Prediction on new vector

test_vector = np.reshape(np.asarray([19,0,27.900,0,1,3]),(1,6))
p = int(regressor.predict(test_vector)[0])
p

import pickle
pickle.dump(model, open('model.pkl','wb'))

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 10. RESULT

10.1 Dataset

10.2 Data Preprocessing:

24
10.3 Data Exploration

10.4 RF Model Result

25
 10.5 LR Model Result

10.6 DTree Model Result

SVR Model Result

26
10.7 Home page:

27
 11 CONCLUSION

In this internship , we proposed a machine learning model for predicting medical costs.. We applied

four regression techniques: Linear Regression, Support Vector Regression, Decision Tree

Regression, and Random Forest Regression. We also applied RF model and observed that age,bmi

are features which decides the dependent variable. Out of all experiments, the Random Forest model

gave better result.

The best indicator for future health care costs are previous costs: the additional history of health

care expenses is known to improve the prediction. Based on this fact, prediction of future health

care costs is better done when patients’ data is known for consecutive periods. At least a two years

history is needed when trying to predict the costs for one year. Our dataset had patients’ monthly

history for 2016 and 2017. However, there are many missing values because most patients had few

claims each year. Therefore, we chose to group claims yearly so that we could have fewer missing

values. This strategy did not work as expected since many patients had data only for 2016. We then

filtered these patients out, and thus, the final set of patients are those who have clinical history for

both 2016 and 2017.so for, future work we can apply this classification process to obtain a patient

risk class as first step to improve the performance of our IEVREG model, and to continue comparing

our model to even more sophisticated methods we could try to solve the prediction of health care

cost using deep learning methods but for this to be feasible we need a larger dataset. We also plan

to apply this model to other regression problems in the health care domain, for example, predicting

the hospital length of stay and predicting the days of readmission based on each patient’s diagnosis

and history, which are two classic prediction problems for this domain.

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 REFERENCES

● Building Machine Learning Powered Applications: Going from Idea to Product,

written by Emmanuel Ameisen
● Hands-On Machine Learning with Scikit-Learn and TensorFlow (2nd
Edition) written by Aurélien Géron
● Introduction of Machine Learning – W3Schools
● Supervised vs Unsupervised Learning - Javatpoint
● Implementation of Flask in Python – GeeksforGeeks

● https://www.ibm.com/topics/machine-learning
● https://www.analyticsvidhya.com/blog/2021/05/prediction-of-health-
expense/
● https://productcoalition.com/difference-between-traditional-programming-
versus-machine-learning-from-a-pm-perspective-3802b02bc7f6
● https://medium.com/@priyapareek0205/machine-learning-algorithms-and-
where-they-are-used-c74de1441e1
● https://www.tutorialspoint.com/python/python_overview.htm
● https://docs.anaconda.com/free/navigator/index.html
● https://www.kaggle.com/datasets/mirichoi0218/insurance

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