I have written this post for the developers and assumes no background in statistics or mathematics. The focus is mainly on how the k-NN algorithm works and how to use it for predictive modeling problems.
Classification of objects is an important area of research and application in a variety of fields. In the presence of full knowledge of the underlying probabilities, Bayes decision theory gives optimal error rates. In those cases where this information is not present, many algorithms make use of distance or similarity among samples as a means of classification.
Resting heart rate data
The article has been divided into 2 parts. Table of content. K-NN or K-Nearest Neighbors is one of the most famous classification algorithms as of now in the industry simply because of its simplicity and accuracy. K-NN is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure e. KNN has been used in statistical estimation and pattern recognition already at the beginning of the s as a non-parametric technique.
The algorithm assumes that similar things exist in close proximity. In other words, entities which are similar exist together. In K-NN, K is the number of nearest neighbors. The number of neighbors is the core deciding factor. K is generally an odd number if the number of classes is 2.
This is the simplest case. First, you find the one closest point to P and then the label of the nearest point assigned to P.
Second, you find the k closest point to P and then classify points by majority vote of its K neighbors. Each object votes for their class and the class with the most votes is taken as the prediction. For finding closest similar points, we find the distance between points using distance measures such as Euclidean distance, Hamming distance, Manhattan distance, and Minkowski distance. The algorithm has the following basic steps:. Three most commonly used distance measures used to calculate the distance between point P and its nearest neighbors are represented as :.
In this article we will go ahead with Euclidean distance, so let's understand it first. Euclidean distance: It is the most commonly used distance measure also called simply distance.
The usage of a Euclidean distance measure is highly recommended when the data is dense or continuous. Euclidean distance is the best proximity measure.
Heart Disease Prediction
The Euclidean distance between two points is the length of the path connecting them. The Pythagorean theorem gives this distance between two points. Below figure shows how to calculate Euclidean distance between two points in a 2-dimensional plane. KNN can be used for both classification and regression predictive problems. However, it is more widely used in classification problems in the industry.Chapter Description: This data file contains nutritional information and grocery shelf location for 77 breakfast cereals.
One gram of fat contains 9 calories, and carbohydrates and proteins contain 4 calories per gram. A "good" diet should also contain grams of dietary fiber. The data include cereal name, cereal manufacturer, number of calories per serving, grams of protein, grams of fat, milligrams of sodium, grams of fiber, grams of carbohydrates, grams of sugars, milligrams of potassium, typical percentage of the FDA's RDA of vitamins, the weight of one serving, and the shelf location 1,2 or 3 for bottom, middle or top.
A variable named "rating" was calculated by Consumer Reports. Details regarding how the rating value was calculated is not available. Dataset 2: Smoking and Cancer Reference: J. Description: The data are per capita numbers of cigarettes smoked sold by 43 states and the District of Columbia in together with death rates per thousand population from various forms of cancer.
Dataset 3: Massachusetts Lunatics Reference: J. Description: These data are from an survey conducted by the Massachusetts Commission on Lunacy under the leadership of Edward Jarvis. Jarvis was President of the American Statistical Association from to Dataset 4: Student t-distribution Description: Charles Darwinauthor of The Origin of Species later investigated the effect of cross-fertilization on the size of plants. Pairs of plants, one cross- and one self-fertilized at the same time and whose parents were grown from the same seed, were planted and grown in the same pot.
The numbers of pairs of plants were not large because the time and care needed to carry out the experiments were substantial. Darwin's experiments had taken 11 years. Darwin had sent the data for several species to his cousin, Francis Galton. Galtonan eminent statistician, was unaware of any rigorous method for making an inference about the mean of a population when its standard deviation was unknown.
Certainly that was the case for Darwin's differences in sizes of pairs of plants. The results of one of Darwin's experiments given by R. Fisher are presented in the data file. Gosset was employed by the Guniess Brewing Company of Dublin. Sample sizes available for experimentation in brewing were necessarily small, and Gosset knew that a correct way of dealing with small samples was needed. Pearson told him the current state of knowledge was unsatisfactory.Creators: 1.
Hungarian Institute of Cardiology. Budapest: Andras Janosi, M. Donor: David W. Aha aha ' ' ics. This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The "goal" field refers to the presence of heart disease in the patient. It is integer valued from 0 no presence to 4. Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence values 1,2,3,4 from absence value 0.
The names and social security numbers of the patients were recently removed from the database, replaced with dummy values. One file has been "processed", that one containing the Cleveland database. All four unprocessed files also exist in this directory. Only 14 attributes used: 1. Detrano, R. International application of a new probability algorithm for the diagnosis of coronary artery disease.
American Journal of Cardiology, 64, Models of incremental concept formation.
Artificial Intelligence, 40, Jeroen Eggermont and Joost N. Kok and Walter A. Genetic Programming for data classification: partitioning the search space. Zhi-Hua Zhou and Yuan Jiang. IEEE Trans.
Data Eng, Remco R. Bouckaert and Eibe Frank. Gavin Brown.The UCI repository contains three datasets on heart disease. Each dataset contains information about several patients suspected of having heart disease such as whether or not the patient is a smoker, the patients resting heart rate, age, sex, etc. The patients were all tested for heart disease and the results of that tests are given as numbers ranging from 0 no heart disease to 4 severe heart disease.
The goal of this notebook will be to use machine learning and statistical techniques to predict both the presence and severity of heart disease from the features given. In addition, I will also analyze which features are most important in predicting the presence and severity of heart disease.
UCI Heart Disease Analysis
There are three relevant datasets which I will be using, which are from Hungary, Long Beach, and Cleveland. Each of these hospitals recorded patient data, which was published with personal information removed from the database. The datasets are slightly messy and will first need to be cleaned. For example,the dataset isn't in standard csv format, instead each feature spans several lines, with each feature being separated by the word 'name'.
I will first process the data to bring it into csv format, and then import it into a pandas df. The data should have 75 rows, however, several of the rows were not written correctly and instead have too many elements.
These rows will be deleted, and the data will then be loaded into a pandas dataframe. The NaN values are represented as These will need to be flagged as NaN values in order to get good results from any machine learning algorithm. However before I do start analyzing the data I will drop columns which aren't going to be predictive.
Several features such as the day of the exercise reading, or the ID of the patient are unlikely to be relevant in predicting heart disease.
The exercise protocol might be predictive, however, since this might vary with the hospital, and since the hospitals had different rates for the category of heart disease, this might end up being more indicative of the hospital the patient went to and not of the likelihood of heart disease. The description of the columns on the UCI website also indicates that several of the columns should not be used. Since I am only trying to predict the presence of heart disease and not the specific vessels which are damaged, I will discard these columns.How To Import Csv Datasets in Python Pandas
To get a better sense of the remaining data, I will print out how many distinct values occur in each of the columns. Some columns such as pncaden contain less than 2 values. These columns are not predictive and hence should be dropped.
There are also several columns which are mostly filled with NaN entries. I will drop any entries which are filled mostly with NaN entries since I want to make predictions based on categories that all or most of the data shares.Download the latest release here.
This returns a numpy. Analysis requires the sampling rate for your data. Access as such:. The toolkit has functionality to open and parse delimited.
This returns a 1-dimensional numpy. The toolkit has a simple built-in sample-rate detection. It can handle ms-based timers and datetime-based timers. A plotting function is included. It plots the original signal and overlays the detected peaks and the rejected peaks if any were rejected.
Measures are only calculated for non-rejected peaks and intervals between two non-rejected peaks. Rejected detections do not influence the calculated measures. By default a plot is visualised when plotter is called.
The function returns a matplotlib. The function has two required arguments:. There may be situations where you have a long heart rate signal, and want to compute how the heart rate measures change over time in the signal.
What this will do is segment the data into sections of 40 seconds each. In this example the last three arguments will be passed on the the process function and used in the analysis. For a full list of arguments that process supports, see the Basic Example. These notebooks show how to handle various analysis tasks with HeartPy, from smartwatch data, smart ring data, regular PPG, and regular and very noisy ECG. We recommend you follow the notebooks in order: - [1. Python Heart Rate Analysis Toolkit latest.
Example with the included data. If not specified, default title is used. Example Notebooks are available for further reading!Didn't find what you're looking for?
Suggest a dataset here. Federal datasets are subject to the U. Federal Government Data Policy. Non-federal participants e. Data policies influence the usefulness of the data. Learn more about how to search for data and use this catalog. Formats: CSV. The primary source of data for this file is State of Connecticut — A listing of each accidental death associated with drug overdose in Connecticut from to A "Y" value under the different substance columns indicates that Before doing any market analysis on property sales, check This dataset contains Raleigh Durham International Airport weather data pulled from Environmental Protection Agency — This annual report is part of the U.
Datasets labeled "Current" contain this month's postings, while those labeled "Archive" contain a running list The Diversity Index shows the likelihood that two persons chosen at random from Records contain College-bound seniors mean SAT scores. Records with These boundaries are approximate and names are not official. The data can be viewed on the City of Bloomington — This set raw of data contains information from Bloomington Police Department regarding guns reported stolen.
Includes information pertaining to land, values, sales, abatements, and building characteristics if City of Bloomington — This data set contains information related to from counters of Traffic Counts and Accidents.
Please contact The Department of Planning and Transportation if you Currently this dataset does not include City of Pittsburgh dogs. State of Oklahoma — Increase the number of eligible children receiving mental health treatment from 87, in to 91, by Constraints: Not to be used for Queryed out ND via attributes and reprojected in ArcMap.A physiologist wants to determine whether a particular running program has an effect on resting heart rate.
The heart rates of 20 randomly selected people were measured. The people were then put on the running program and measured again one year later. Thus, the before and after measurements for each person are a pair of observations. Resting heart rate data. You can use this data to demonstrate Paired t. Worksheet column Description Before The resting heart rate of the person before the running program After The resting heart rate of the person after the running program Difference The difference between the person's resting heart rate before and after the running program Download RestingHeartRate.
Read our policy. The resting heart rate of the person before the running program. The resting heart rate of the person after the running program. The difference between the person's resting heart rate before and after the running program.