Disease Prediction and Attribute Analyzation Using Machine Learning

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Introduction

I approached this project to learn about the Core Concept of Machine learning environment Under the Guidance : Dr. Rohit Gupta Sir . With this in mind,I have learned Machine Learing project lifecycle .Studied about different datasets and algorithms of Machine learning and also got idea how different values of parameter effects performance of Algorithm . For Better User Interface experience and designs I taken help of Streamlit.io which is open source app framework which supports Python language.

Aim of Project

Aim of the Project is to Analyse Different parameters of various Disease datasets and analyzes the data to provides prediction of Disease using different algorithm and also computes accuracy for the various classification algorithms used i.e

1. Logistic Regression

2. Random Forest

3. K-Nearest Neighbor

Datasets Used :

1. Breast Cancer Dataset :

Worldwide, breast cancer is the most common type of cancer in women and the second highest in terms of mortality rates.Diagnosis of breast cancer is performed when an abnormal lump is found (from self-examination or x-ray) or a tiny speck of calcium is seen(on an x-ray). After a suspicious lump is found, the doctor will conduct a diagnosis to determine whether it is cancerous and,if so, whether it has spread to other parts of the body.This breast cancer dataset was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg.

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2. Cardiovascular Disease dataset :

The dataset consists of 70,000 records of patients data in 12 features, such as age, gender, systolic blood pressure, diastolic blood pressure, and etc. The target class "cardio" equals to 1, when patient has cardiovascular desease, and its 0 if patient is healthy.

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3. Diabetes Dataset :

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. All the patients here are female 21 years or older.It contains the following columns: Pregnancies: Number of times pregnant Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test BloodPressure: Diastolic blood pressure (mm Hg) SkinThickness: Triceps skin fold thickness (mm)Insulin: 2-Hour serum insulin (mu U/ml) BMI: Body mass index (weight in kg/(height in m)^2)DiabetesPedigreeFunction: Diabetes pedigree function Age: Age (years) Outcome: Class variable (0 or 1))

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User Interface :

For Better User Interface experience and designs we have taken help from Streamlit which is open source app framework in Python language. Live version of Project is Deployed on Streamlit Cloud.