基于Spark的心脏病分析

一、项目开发目的

当今社会发展迅速，生活节奏也随着经济的发展越来越快，不少年轻人由于工作繁重同时缺少锻炼、饮食作息不规律，生活步入范亚健康化。腰腿疼痛、胃不舒服等症状我们平时都能明显地感受到并加以治疗。而心脏疾病却是难以捕捉，如果没有定期体检的习惯，很难直观地感受到心脏的潜在风险。心脏类疾病的发作迅速，后果严重。Framingham Heart Study（FHS）针对心脏疾病开展了长达70多年的跟踪研究。其中有一项长达26年的大规模研究显示，在全猝死患者中，心源性猝死高达75%。在现实中，我们也经常能看到关于年轻人猝死的新闻。所以本项目旨在对心脏病相关数据进行分析、建模，让大家对于心脏疾病更为了解、更加关注自身的身体健康。主要目的如下：1、对于心脏病相关数据集进行数据清洗、处理、特征筛选。2、使用分析得到的数据进行建模。3、通过网页将心脏病相关数据可视化呈现，并且支持通过输入个人信息查询心脏病患病风险。其中，我主要负责的是数据处理与清洗部分，本文对其他部分仅进行大致阐述。

工程文件:https://github.com/zhuozhuo233/Heart-disease-analysis

二、数据来源及相关解释

本次项目的数据集来自UCI开放数据集以及来自于Kaggle平台的公开数据集。

原始数据集解释如下：

Heart数据集

名词解释：

属性	解释	注释
age	年龄	29-77
sex	性别	1男、0女
cp	胸痛类型	0无症状、1典型心绞痛、2非典型心绞痛、3非心绞痛
trestbps	静息血压	94-200
chol	胆固醇（mg/dl）	126-564
fbs	空腹血糖>120mg/dl	0否、1是
restecg	静息心电图结果	0正常、1有异常、2左心室肥大
thalach	最大心率	71-202
exang	运动引起的心绞痛	0没有、1有过
oldpeak	运动相对于休息时引起的ST段下降	0-0.62
slope	最高运动ST段的斜率	0平、1升、2降
ca	使用荧光染色法测定的主血管数
thal	地中海贫血症状	1正常、2固定缺陷、3可逆缺陷
target	是否患有心脏病	0否、1是

Cardio数据集

属性	解释	注释
id	编号
age	年龄
gender	性别	1女、2男
height	身高（cm）
weight	体重（kg）
ap_hi	收缩压
ap_lo	舒张压
cholesterol	胆固醇	1正常、2高于正常、3远高于正常
gluc	血糖	1正常、2高于正常、3远高于正常
smoke	是否有吸烟习惯	0没有、1有
alco	是否有饮酒习惯	0没有、1有
active	是否有锻炼习惯	0没有、1有
cardio	是否存在心血管疾病	0没有、1有

三、工程文件解释

1.数据处理部分

Cardio工程

Partition：

将表中的数据按列分别存为json文件以供后续使用

import org.apache.spark.sql.SparkSession

object partition {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().enableHiveSupport().getOrCreate()
    spark.sparkContext.setLogLevel("WARN")

    val cardio = spark.read.table("cardio.cardio")

    val id = cardio.select("id")
    id.write.mode("overwrite").json("/user/root/cardio/id")

    val age = cardio.select("age")
    age.write.mode("overwrite").json("/user/root/cardio/age")

    val gender = cardio.select("gender")
    gender.write.mode("overwrite").json("/user/root/cardio/gender")

    val ap_hi = cardio.select("ap_hi")
    ap_hi.write.mode("overwrite").json("/user/root/cardio/ap_hi")

    val ap_lo = cardio.select("ap_lo")
    ap_lo.write.mode("overwrite").json("/user/root/cardio/ap_lo")

    val cholesterol = cardio.select("cholesterol")
    cholesterol.write.mode("overwrite").json("/user/root/cardio/cholesterol")

    val gluc = cardio.select("gluc")
    gluc.write.mode("overwrite").json("/user/root/cardio/gluc")

    val smoke = cardio.select("smoke")
    smoke.write.mode("overwrite").json("/user/root/cardio/smoke")

    val alco = cardio.select("alco")
    alco.write.mode("overwrite").json("/user/root/cardio/alco")

    val active = cardio.select("active")
    active.write.mode("overwrite").json("/user/root/cardio/active")

    val cardio1 = cardio.select("cardio")
    cardio1.write.mode("overwrite").json("/user/root/cardio/cardio1")
  }

}

Ap:

​ 分析出心脏病患者中收缩压、舒张压正常的比例与心脏病患者收缩压与舒张压的数值分布情况

import org.apache.spark.sql.SparkSession


object ap {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().enableHiveSupport().getOrCreate()
    spark.sparkContext.setLogLevel("WARN")

    val cardio = spark.read.table("cardio.cardio")

    val ap_hi = cardio.select("ap_hi").filter("cardio = '1'").count()
    val ap_hi_1 = cardio.select("ap_hi").filter("cardio = '1'").filter("ap_hi >= '90' and ap_hi <= '140'").count()
    val ap_hiresult = (ap_hi_1.toFloat/ap_hi).formatted("%.2f").toFloat
    println("心脏病患者中收缩压正常的比率:")
    rate(ap_hiresult)

    val ap_lo = cardio.select("ap_lo").filter("cardio = '1'").count()
    val ap_lo_1 = cardio.select("ap_lo").filter("cardio = '1'").filter("ap_lo >= '60' and ap_hi <= '90'").count()
    val ap_loresult = (ap_lo_1.toFloat/ap_lo).formatted("%.2f").toFloat
    println("心脏病患者中舒张压正常的比率:")
    rate(ap_loresult)

    val ap_hi100_110 = cardio.filter("cardio = '1'").filter("ap_hi >= '100' and ap_hi <='110'").count()
    val result100_110 = (ap_hi100_110.toFloat/ap_hi).formatted("%.2f").toFloat
    println("收缩压在100~110mmHg：")
    rate(result100_110)

    val ap_hi110_120 = cardio.filter("cardio = '1'").filter("ap_hi >= '111' and ap_hi <='120'").count()
    val result110_120 = (ap_hi110_120.toFloat/ap_hi).formatted("%.2f").toFloat
    println("收缩压在110~120mmHg：")
    rate(result110_120)

    val ap_hi120_130 = cardio.filter("cardio = '1'").filter("ap_hi >= '121' and ap_hi <='130'").count()
    val result120_130 = (ap_hi120_130.toFloat/ap_hi).formatted("%.2f").toFloat
    println("收缩压在120~130mmHg：")
    rate(result120_130)

    val ap_hi130_140 = cardio.filter("cardio = '1'").filter("ap_hi >= '131' and ap_hi <='140'").count()
    val result130_140 = (ap_hi130_140.toFloat/ap_hi).formatted("%.2f").toFloat
    println("收缩压在130~140mmHg：")
    rate(result130_140)

    val ap_hi140_150 = cardio.filter("cardio = '1'").filter("ap_hi >= '141' and ap_hi <='150'").count()
    val result140_150 = (ap_hi140_150.toFloat/ap_hi).formatted("%.2f").toFloat
    println("收缩压在140~150mmHg：")
    rate(result140_150)

    val ap_hi150_160 = cardio.filter("cardio = '1'").filter("ap_hi >= '151' and ap_hi <='160'").count()
    val result150_160 = (ap_hi150_160.toFloat/ap_hi).formatted("%.2f").toFloat
    println("收缩压在150~160mmHg：")
    rate(result150_160)

    val ap_hi160_170 = cardio.filter("cardio = '1'").filter("ap_hi >= '161' and ap_hi <='170'").count()
    val result160_170 = (ap_hi160_170.toFloat/ap_hi).formatted("%.2f").toFloat
    println("收缩压在160~170mmHg：")
    rate(result160_170)

    val ap_hi170_180 = cardio.filter("cardio = '1'").filter("ap_hi >= '171' and ap_hi <='180'").count()
    val result170_180 = (ap_hi170_180.toFloat/ap_hi).formatted("%.2f").toFloat
    println("收缩压在170~180mmHg：")
    rate(result170_180)

    val ap_hi180_190 = cardio.filter("cardio = '1'").filter("ap_hi >= '181' and ap_hi <='190'").count()
    val result180_190 = (ap_hi180_190.toFloat/ap_hi).formatted("%.2f").toFloat
    println("收缩压在180~190mmHg：")
    rate(result180_190)

    val ap_hi190_200 = cardio.filter("cardio = '1'").filter("ap_hi >= '191' and ap_hi <='200'").count()
    val result190_200 = (ap_hi190_200.toFloat/ap_hi).formatted("%.2f").toFloat
    println("收缩压在190~200mmHg：")
    rate(result190_200)
//   ----------------舒张压-------------------------------
    val ap_lo60_70 = cardio.filter("cardio = '1'").filter("ap_lo >= '60' and ap_lo <='70'").count()
    val result60_70 = (ap_lo60_70.toFloat/ap_lo).formatted("%.2f").toFloat
    println("舒张压在60~70mmHg：")
    rate(result60_70)

    val ap_lo70_80 = cardio.filter("cardio = '1'").filter("ap_lo >= '71' and ap_lo <='80'").count()
    val result70_80 = (ap_lo70_80.toFloat/ap_lo).formatted("%.2f").toFloat
    println("舒张压在70~80mmHg：")
    rate(result70_80)

    val ap_lo80_90 = cardio.filter("cardio = '1'").filter("ap_lo >= '81' and ap_lo <='90'").count()
    val result80_90 = (ap_lo80_90.toFloat/ap_lo).formatted("%.2f").toFloat
    println("舒张压在80~90mmHg：")
    rate(result80_90)

    val ap_lo90_100 = cardio.filter("cardio = '1'").filter("ap_lo >= '91' and ap_lo <='100'").count()
    val result90_100 = (ap_lo90_100.toFloat/ap_lo).formatted("%.2f").toFloat
    println("舒张压在90~100mmHg：")
    rate(result90_100)

    val ap_lo100_110 = cardio.filter("cardio = '1'").filter("ap_lo >= '101' and ap_lo <='110'").count()
    val resultlo100_110 = (ap_lo100_110.toFloat/ap_lo).formatted("%.2f").toFloat
    println("舒张压在100~110mmHg：")
    rate(resultlo100_110)

    val ap_lo110_120 = cardio.filter("cardio = '1'").filter("ap_lo >= '111' and ap_lo <='120'").count()
    val resultlo110_120 = (ap_lo110_120.toFloat/ap_lo).formatted("%.2f").toFloat
    println("舒张压在110~120mmHg：")
    rate(resultlo110_120)
  }

  def rate(x:Float):Unit ={
    var per = (x * 100).toInt
    println(per)
  }

}

Bmi:

​ 找出性别与相对的bmi数据，生成为json文件以供后续使用

import org.apache.spark.sql.SparkSession



object bmi {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().enableHiveSupport().getOrCreate()
    spark.sparkContext.setLogLevel("WARN")

    val cardio = spark.read.table("cardio.cardio3")

    val bmi = cardio.select("gender","bmi")
    bmi.write.mode("overwrite").json("/user/root/cardio/age_bmi")


  }

}

ageprocess:

​ 从年龄入手，寻找年龄的最值，并且分析出不同年龄段患病的比例并生成为json文件

import org.apache.spark.sql.SparkSession

object ageprocess {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().enableHiveSupport().getOrCreate()
    spark.sparkContext.setLogLevel("WARN")

    val cardio = spark.read.table("cardio.cardio")

    cardio.selectExpr("max(age) as max_age").show()
    cardio.selectExpr("min(age) as min").show()

    val agetotal = cardio.select("age").count()


    val age30_35 = cardio.filter("age >= '30' and age <='35'").filter("cardio = '1'").count()
    val result30_35 = (age30_35.toFloat/agetotal).formatted("%.2f").toFloat
    println("30到35岁比率：")
    rate(result30_35)

    val age36_40 = cardio.filter("age >= '36' and age <='40'").filter("cardio = '1'").count()
    val result30_40 = (age36_40.toFloat/agetotal).formatted("%.2f").toFloat
    println("36到40岁比率：")
    rate(result30_40)

    val age41_45 = cardio.filter("age >= '41' and age <='45'").filter("cardio = '1'").count()
    val result41_45 = (age41_45.toFloat/agetotal).formatted("%.2f").toFloat
    println("41到45岁比率：")
    rate(result41_45)

    val age46_50 = cardio.filter("age >= '46' and age <='50'").filter("cardio = '1'").count()
    val result46_50 = (age46_50.toFloat/agetotal).formatted("%.2f").toFloat
    println("46到50岁比率：")
    rate(result46_50)

    val age51_55 = cardio.filter("age >= '51' and age <='55'").filter("cardio = '1'").count()
    val result51_55 = (age51_55.toFloat/agetotal).formatted("%.2f").toFloat
    println("51到55岁比率：")
    rate(result51_55)

    val age56_60 = cardio.filter("age >= '56' and age <='60'").filter("cardio = '1'").count()
    val result56_60 = (age56_60.toFloat/agetotal).formatted("%.2f").toFloat
    println("56到60岁比率：")
    rate(result56_60)

    val age61_65 = cardio.filter("age >= '61' and age <='65'").filter("cardio = '1'").count()
    val result61_65 = (age61_65.toFloat/agetotal).formatted("%.2f").toFloat
    println("60到65岁比率：")
    rate(result61_65)

  }
  def rate(x:Float):Unit ={
    var per = (x * 100).toInt
    println(per)
  }
}

Exam:

​ 找出数据集中心脏病患者胆固醇与正常值的比较情况并打印

​ 找出数据集中心脏病患者血糖与正常值的比较情况并打印

import org.apache.spark.sql.SparkSession

object exam {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().enableHiveSupport().getOrCreate()
    spark.sparkContext.setLogLevel("WARN")

    val cardio = spark.read.table("cardio.cardio")

    val cho = cardio.select("cholesterol").filter("cardio = '1'").count()
    val cho1 = cardio.select("cholesterol").filter("cholesterol = '1'").filter("cardio = '1'").count()
    val cho2 = cardio.select("cholesterol").filter("cholesterol = '2'").filter("cardio = '1'").count()
    val cho3 = cardio.select("cholesterol").filter("cholesterol = '3'").filter("cardio = '1'").count()

    val smokeresult1 = (cho1.toFloat/cho).formatted("%.2f").toFloat
    println("心脏病患者中胆固醇数值等于正常的比例:")
    rate(smokeresult1)

    val smokeresult2 = (cho2.toFloat/cho).formatted("%.2f").toFloat
    println("心脏病患者中胆固醇数值高于正常的比例:")
    rate(smokeresult2)

    val smokeresult3 = (cho3.toFloat/cho).formatted("%.2f").toFloat
    println("心脏病患者中胆固醇数值远高于正常的比例:")
    rate(smokeresult3)

    val gluc = cardio.select("gluc").filter("cardio = '1'").count()
    val gluc1 = cardio.select("gluc").filter("gluc = '1'").filter("cardio = '1'").count()
    val gluc2 = cardio.select("gluc").filter("gluc = '2'").filter("cardio = '1'").count()
    val gluc3 = cardio.select("gluc").filter("gluc = '3'").filter("cardio = '1'").count()

    val glucresult1 = (gluc1.toFloat/gluc).formatted("%.2f").toFloat
    println("心脏病患者中血糖数值等于正常的比例:")
    rate(glucresult1)

    val glucresult2 = (gluc2.toFloat/gluc).formatted("%.2f").toFloat
    println("心脏病患者中血糖数值高于正常的比例:")
    rate(glucresult2)

    val glucresult3 = (gluc3.toFloat/gluc).formatted("%.2f").toFloat
    println("心脏病患者中血糖数值远高于正常的比例:")
    rate(glucresult3)




  }
  def rate(x:Float):Unit ={
    var per = (x * 100).toInt
    println(per)
  }


}

Hobbys:

​ 计算出心脏病患者中有吸烟、饮酒、锻炼习惯的比例

import org.apache.spark.sql.SparkSession

object hobbys {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().enableHiveSupport().getOrCreate()
    spark.sparkContext.setLogLevel("WARN")

    val cardio = spark.read.table("cardio.cardio")

    val smoke = cardio.select("smoke").filter("cardio = '1'").count()
    val issmoke = cardio.select("smoke").filter("smoke = '1'").filter("cardio = '1'").count()
    val smokeresult = (issmoke.toFloat/smoke).formatted("%.2f").toFloat
    println("心脏病患者中有吸烟习惯的比例:")
    rate(smokeresult)

    val alco = cardio.select("alco").filter("cardio = '1'").count()
    val isalco = cardio.select("alco").filter("alco = '1'").filter("cardio = '1'").count()
    val alcoresult = (isalco.toFloat/alco).formatted("%.2f").toFloat
    println("心脏病患者中有饮酒习惯的比例:")
    rate(alcoresult)

    val active = cardio.select("active").filter("cardio = '1'").count()
    val isactive = cardio.select("active").filter("active = '1'").filter("cardio = '1'").count()
    val activeresult = (isactive.toFloat/active).formatted("%.2f").toFloat
    println("心脏病患者中有锻炼习惯的比例:")
    rate(activeresult)



  }
  def rate(x:Float):Unit ={
    var per = (x * 100).toInt
    println(per)
  }

}

Ifcardio：

​ 找出数据集中年龄与是否患病的信息，生成为json文件以供后续使用

import org.apache.spark.sql.SparkSession

object ifcardio {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().enableHiveSupport().getOrCreate()
    spark.sparkContext.setLogLevel("WARN")

    val cardio = spark.read.table("cardio.cardio3")

    val cardio1 = cardio.select("age", "cardio")
    cardio1.write.mode("overwrite").json("/user/root/cardio/age_cardio")
  }

}

Heart工程

Ageprocess:

​ 根据数据集分析并计算出不同年龄段的患病风险并打印

import org.apache.spark.sql.SparkSession

object ageprocess {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().enableHiveSupport().getOrCreate()
    spark.sparkContext.setLogLevel("WARN")

    val heart = spark.read.table("heart.heart")

//    val age29_40 = heart.filter("age >= '29' and age <= '40'").show()
    val age29_40_1 = heart.filter("age >= '29' and age <= '40'").filter("target = '1'").count()
    val age29_40_2 = heart.filter("age >= '29' and age <= '40'").count()
    val result29_40 = (age29_40_1.toFloat/age29_40_2).formatted("%.2f").toFloat
    println("29到40岁患病风险:")
    baifenshu(result29_40)

    val age41_50_1 = heart.filter("age >= '41' and age <= '50'").filter("target = '1'").count()
    val age41_50_2 = heart.filter("age >= '41' and age <= '50'").count()
    val result41_50 = (age41_50_1.toFloat/age41_50_2).formatted("%.2f").toFloat
    println("41到50岁患病风险:")
    baifenshu(result41_50)

    val age51_60_1 = heart.filter("age >= '51' and age <= '60'").filter("target = '1'").count()
    val age51_60_2 = heart.filter("age >= '51' and age <= '60'").count()
    val result51_60 = (age51_60_1.toFloat/age51_60_2).formatted("%.2f").toFloat
    println("51到60岁患病风险:")
    baifenshu(result51_60)

    val age61_70_1 = heart.filter("age >= '61' and age <= '70'").filter("target = '1'").count()
    val age61_70_2 = heart.filter("age >= '61' and age <= '70'").count()
    val result61_70 = (age61_70_1.toFloat/age61_70_2).formatted("%.2f").toFloat
    println("61到70岁患病风险:")
    baifenshu(result61_70)

    val age71_77_1 = heart.filter("age >= '71' and age <= '77'").filter("target = '1'").count()
    val age71_77_2 = heart.filter("age >= '71' and age <= '77'").count()
    val result71_77 = (age71_77_1.toFloat/age71_77_2).formatted("%.2f").toFloat
    println("70到71岁患病风险:")
    baifenshu(result71_77)
  }

  def baifenshu(x: Float ): Unit ={
    var per = (x * 100).toInt
    println(per + "%")
  }
}

Cpprocess:

​ 选取出数据集中年龄，胸痛类型、是否患病标签写入新的json文件以供后续分析其关联

import org.apache.spark.sql.SparkSession


object cpprocess {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().enableHiveSupport().getOrCreate()
    spark.sparkContext.setLogLevel("WARN")

    val heart = spark.read.table("heart.heart")

    val cp = heart.select("age","cp","target")
    cp.write.mode("overwrite").json("/user/root/heart/age_cp_target")
  }
}

Exangprocess：

​ 选取出数据集中是否有过运动引起的心绞痛与是否患病特征，写入新的json文件以供后续分析其关联使用。

import org.apache.spark.sql.SparkSession

object exangprocess {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().enableHiveSupport().getOrCreate()
    spark.sparkContext.setLogLevel("WARN")

    val heart = spark.read.table("heart.heart")

    val exang = heart.select("exang","target")
    exang.write.mode("overwrite").json("/user/root/heart/exang_target")
  }
}

Thalach_target:

​ 选取出数据集中最大心率与是否患病数据，写入新的文件以供后续分析其关联使用

import org.apache.spark.sql.SparkSession

object thalach_target {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().enableHiveSupport().getOrCreate()
    spark.sparkContext.setLogLevel("WARN")

    val heart = spark.read.table("heart.heart")

    val thalach = heart.select("thalach","target")
    thalach.write.mode("overwrite").json("/user/root/heart/thalach_target")
  }
}

Thalachprocess:

​ 将最高心率进行分段统计分析，打印出在分段内的统计人数与患病概率

import org.apache.spark.sql.SparkSession

object thalachprocess {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().enableHiveSupport().getOrCreate()
    spark.sparkContext.setLogLevel("WARN")

    val heart = spark.read.table("heart.heart")

    //    val age29_40 = heart.filter("age >= '29' and age <= '40'").show()
    val thalach71_90_1 = heart.filter("thalach >= '71' and thalach <= '90'").filter("target = '1'").count()
    val thalach71_90_2 = heart.filter("thalach >= '71' and thalach <= '90'").count()
    val thalach71_90 = (thalach71_90_1.toFloat/thalach71_90_2).formatted("%.2f").toFloat
    println("最高心率在71到90每分钟的患病概率:")
    baifenshu(thalach71_90)
    println("最高心率在71到90每分钟的统计人数：")
    println(thalach71_90_2)


    val thalach91_110_1 = heart.filter("thalach >= '91' and thalach <= '110'").filter("target = '1'").count()
    val thalach91_110_2 = heart.filter("thalach >= '91' and thalach <= '110'").count()
    val thalach91_110 = (thalach91_110_1.toFloat/thalach91_110_2).formatted("%.2f").toFloat
    println("最高心率在91到110每分钟的患病概率:")
    baifenshu(thalach91_110)
    println("最高心率在91到110每分钟的统计人数：")
    println(thalach91_110_2)

    val thalach111_130_1 = heart.filter("thalach >= '111' and thalach <= '130'").filter("target = '1'").count()
    val thalach111_130_2 = heart.filter("thalach >= '111' and thalach <= '130'").count()
    val thalach111_130 = (thalach111_130_1.toFloat/thalach111_130_2).formatted("%.2f").toFloat
    println("最高心率在111到130每分钟的患病概率:")
    baifenshu(thalach111_130)
    println("最高心率在111到130每分钟的统计人数：")
    println(thalach111_130_2)

    val thalach131_150_1 = heart.filter("thalach >= '131' and thalach <= '150'").filter("target = '1'").count()
    val thalach131_150_2 = heart.filter("thalach >= '131' and thalach <= '150'").count()
    val thalach131_150 = (thalach131_150_1.toFloat/thalach131_150_2).formatted("%.2f").toFloat
    println("最高心率在131到150每分钟的患病概率:")
    baifenshu(thalach131_150)
    println("最高心率在131到150每分钟的统计人数：")
    println(thalach131_150_2)

    val thalach151_170_1 = heart.filter("thalach >= '151' and thalach <= '170'").filter("target = '1'").count()
    val thalach151_170_2 = heart.filter("thalach >= '151' and thalach <= '170'").count()
    val thalach151_170 = (thalach151_170_1.toFloat/thalach151_170_2).formatted("%.2f").toFloat
    println("最高心率在151到170每分钟的患病概率:")
    baifenshu(thalach151_170)
    println("最高心率在151到170每分钟的统计人数：")
    println(thalach151_170_2)

    val thalach171_190_1 = heart.filter("thalach >= '171' and thalach <= '190'").filter("target = '1'").count()
    val thalach171_190_2 = heart.filter("thalach >= '171' and thalach <= '190'").count()
    val thalach171_190 = (thalach171_190_1.toFloat/thalach171_190_2).formatted("%.2f").toFloat
    println("最高心率在171到190每分钟的患病概率:")
    baifenshu(thalach171_190)
    println("最高心率在171到190每分钟的统计人数：")
    println(thalach171_190_2)

    val thalach191_202_1 = heart.filter("thalach >= '191' and thalach <= '202'").filter("target = '1'").count()
    val thalach191_202_2 = heart.filter("thalach >= '191' and thalach <= '202'").count()
    val thalach191_202 = (thalach191_202_1.toFloat/thalach171_190_2).formatted("%.2f").toFloat
    println("最高心率在191到202每分钟的患病概率:")
    baifenshu(thalach191_202)
    println("最高心率在191到202每分钟的统计人数：")
    println(thalach191_202_2)
  }
  def baifenshu(x: Float ): Unit ={
    var per = (x * 100).toInt
    println(per + "%")
  }
}

四、数据预处理及基本分析部分

HDFS数据库一览

HDFS文件存储一览

/user/root

部分项目提交集群运行结果展示

spark-submit --master spark://master:7077 --class test.ageprocess /opt/cardio.jar

spark-submit --master spark://master:7077 --class test.hobbys /opt/cardio.jar

spark-submit --master spark://master:7077 --class test.exam /opt/cardio.jar

spark-submit --master spark://master:7077 --class test.ap /opt/cardio.jar

部分生成的json文件展示

/user/root/cardio

/user/root/heart

五、关于数据建模的解释以及思考

原本希望通过数据建模完成心脏病预测系统：输入数据——>模型比对分析——>输出患心血管疾病的概率

经过数周的思考与对于技术的深入了解，我们认为我们的模型训练仅仅从数字入手，按照目前的知识技术进行是不具备足够的科学性的。真正的心血管疾病预测需要更大量的数据参与训练以及更严格、准确的指标标准。想要完成真正的疾病预测，少不了医疗专业领域的专家参与。遂将我们的目标改为心血管分析可视化，预测系统暂时搁置、希望将来有机会能把这个项目完善。

六、数据可视化展示

组件支持

• HTML

• CSS

• JavaScript

• JQuery

• Ajax

• Echarts

主显界面

预测交互界面（demo）

附加的bmi计算小功能（demo）